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WO2018192491A1 - Procédé et dispositif de campagne d'informations - Google Patents

Procédé et dispositif de campagne d'informations Download PDF

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Publication number
WO2018192491A1
WO2018192491A1 PCT/CN2018/083378 CN2018083378W WO2018192491A1 WO 2018192491 A1 WO2018192491 A1 WO 2018192491A1 CN 2018083378 W CN2018083378 W CN 2018083378W WO 2018192491 A1 WO2018192491 A1 WO 2018192491A1
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WO
WIPO (PCT)
Prior art keywords
information
target
push information
product
product information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2018/083378
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English (en)
Chinese (zh)
Inventor
赵夕炜
徐夙龙
胡景贺
江雪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
Original Assignee
Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Jingdong Century Trading Co Ltd, Beijing Jingdong Shangke Information Technology Co Ltd filed Critical Beijing Jingdong Century Trading Co Ltd
Publication of WO2018192491A1 publication Critical patent/WO2018192491A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/55—Push-based network services
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network

Definitions

  • the present application relates to the field of computer technologies, and in particular, to the field of Internet technologies, and in particular, to an information push method and apparatus.
  • the existing information push method usually loads various push information directly at a certain fixed position of the search result, and the push information is greatly different from the search result, so that there is a problem that the information push is not targeted.
  • the purpose of the embodiments of the present application is to provide an improved information pushing method and apparatus to solve the technical problems mentioned in the above background art.
  • an embodiment of the present application provides an information pushing method, which includes: receiving a product information query request that includes a search term sent by a client; and extracting multiple product information and multiple candidate pushes that match the search term.
  • Information inputting each product information and each candidate push information into a pre-trained order rate prediction model, obtaining an order rate corresponding to each product information and each candidate push information, and inputting each candidate push information to the pre-trained click
  • the rate prediction model obtains a click rate corresponding to each candidate push information, wherein the order rate prediction model is used to represent the correspondence between the product information or the candidate push information and the order rate, and the click rate prediction model is used to represent the candidate push information and Corresponding relationship of click rates; determining target push information in the plurality of candidate push information based on the obtained order rate and click rate; pushing the target push information and the plurality of product information to the client.
  • determining target push information in the plurality of candidate push information based on the obtained order rate and click rate including: for each of the plurality of product information, corresponding to the product information The order rate is used as the order rate threshold, and the candidate push information of the plurality of candidate push information and the order rate is greater than the order rate threshold is selected, and the candidate push information set matching the product information is generated; for each generated a candidate push information set, determining a first expected value of each candidate push information in the candidate push information set, wherein a first expected value of each candidate push information is a click rate corresponding to the candidate push information and a preset a product of the charge value corresponding to the candidate push information; and based on the obtained first expected value, the target push information in the plurality of candidate push information is determined.
  • each of the plurality of product information has a presentation order identifier for indicating a presentation order of the product information, and each candidate push information in the candidate push information set corresponding to the product information is presented Order identification.
  • determining the target push information in the plurality of candidate push information based on the obtained first expected value comprises: using the candidate push information having the largest first expected value among the respective candidate push information sets as the target candidate push information. Generating a target candidate push information set; for each target candidate push information in the target candidate push information set, acquiring a preset order coefficient corresponding to the presentation order indicated by the presentation order identifier carried by the target candidate push information And determining a second expected value of the target candidate push information, wherein the second expected value is a product of a first expected value of the target candidate push information and a sequence coefficient corresponding to the target candidate push information; and a target candidate that maximizes the second expected value
  • the push information is determined to be the target push information.
  • the method further comprises: determining a presentation order indicated by the presentation order identifier carried by the target push information, and determining the determined presentation order as the target presentation order; determining each of the plurality of product information The presentation order indicated by the information indicates the presentation order indicated, and for each product information whose presentation order is not less than the target presentation order, the presentation order of the product information is increased by a first preset value;
  • pushing the target push information and the plurality of product information to the client comprises: sorting the plurality of product information and the target product information in order of presentation from small to large; generating the plurality of products including the sorted Web page for information and target product information; send the web page to the client.
  • the method before sorting the plurality of product information and the target product information in an order of presentation from small to large, the method further comprises: determining that a presentation order of the plurality of product information is less than a preset presentation order threshold The name of the category of the category to which the product information belongs, and the name of the category of the category to which the target push information belongs; the category name of the category to which the determined product information belongs is matched with the name of the category of the category to which the target push information belongs.
  • each of the plurality of product information includes a product name
  • the target product information includes a target product name
  • the method further includes: determining, for each product information of the plurality of product information that the presentation order is less than the preset presentation order threshold, the similarity between the product name in the product information and the target product name in the target push information; The determined similarity is less than a preset similarity threshold, and the product information is determined as the difference product information; and in response to determining that the quantity of the difference product information is greater than the preset number threshold, the display order of the target push information is increased by a third preset value.
  • the method before receiving the information query request sent by the client, the method further includes: extracting first feature information from the preset first training sample, wherein the first training sample includes The order identification of the order situation corresponding to the training sample; using the machine learning algorithm, based on the first feature information and the order identification, the training obtains the order rate prediction model.
  • the method before receiving the information query request sent by the client, the method further includes: extracting second feature information from the preset second training sample, wherein the second training sample includes The click identifier of the click condition corresponding to the training sample; using the machine learning algorithm, the click rate prediction model is trained based on the second feature information and the click mark.
  • an embodiment of the present application provides an information pushing apparatus, where the apparatus includes: a receiving unit configured to receive a product information query request that includes a search term sent by a client; and a first extracting unit configured to extract and Searching for a plurality of product information and a plurality of candidate push information; the input unit is configured to input each product information and each candidate push information into a pre-trained order rate prediction model, and obtain various product information and each candidate Pushing the corresponding order rate of the information, and inputting each candidate push information to the pre-trained click rate prediction model to obtain a click rate corresponding to each candidate push information, wherein the order rate prediction model is used to represent the product information or the candidate push The correspondence between the information and the order rate, the click rate prediction model is used to represent the correspondence between the candidate push information and the click rate; the first determining unit is configured to determine a plurality of candidate pushes based on the obtained order rate and the click rate.
  • Target push information in the message push unit configured to push the target information and multiple Product information to
  • the determining unit includes: a first generating module configured to select, according to each of the plurality of product information, a billing rate corresponding to the product information as a billing rate threshold, and select a plurality of candidates The candidate push information in the push information that has a higher order rate than the order rate threshold, generates a candidate push information set that matches the product information; and the first determining module is configured to use, for each candidate push information set generated, Determining a first expected value of each candidate push information in the candidate push information set, wherein a first expected value of each candidate push information is a click rate corresponding to the candidate push information and a preset corresponding to the candidate push information a product of the billing value; the second determining module is configured to determine target push information in the plurality of candidate push information based on the obtained first expected value.
  • each of the plurality of product information has a presentation order identifier for indicating a presentation order of the product information, and each candidate push information in the candidate push information set corresponding to the product information is presented Order identification.
  • the second determining module includes: a generating submodule configured to use the candidate push information with the first expected value being the largest among the candidate push information sets as the target candidate push information, to generate the target candidate push information set; a determining submodule configured to: for each target candidate push information in the target candidate push information set, obtain a preset order coefficient corresponding to the presentation order indicated by the presentation order identifier carried by the target candidate push information And determining a second expected value of the target candidate push information, wherein the second expected value is a product of a first expected value of the target candidate push information and a sequence coefficient corresponding to the target candidate push information; and the second determining submodule is configured The target candidate push information that maximizes the second expected value is determined as the target push information.
  • the apparatus further includes: a second determining unit configured to determine a presentation order indicated by the presentation order identifier carried by the target push information, and determine the determined presentation order as the target presentation order; An adding unit configured to determine a presentation order indicated by a presentation order identifier carried by each of the plurality of product information, and to display the product information for each product information whose presentation order is not less than the target presentation order The order is increased by a first preset value;
  • the pushing unit includes: a sorting module configured to sort the plurality of product information and the target product information in an order of presentation from small to large; and the second generating module is configured to generate the content including the sorted a web page of product information and target product information; a sending module configured to send a web page to the client.
  • the apparatus further includes: a third determining unit configured to determine a category name of the category of the product information in which the presentation order is less than the preset presentation order threshold, and determine the target push The name of the category of the category to which the information belongs; the matching unit is configured to match the determined category name of the category of each product information with the category name of the category to which the target push information belongs; the fourth determining unit is configured Determining the number of product information whose category name matches the category name of the category to which the target push information belongs, and determining the total number of the plurality of product information; and the second adding unit configured to respond to the determined quantity and the total quantity The ratio is less than the preset ratio, and the presentation order of the target push information is increased by a second preset value.
  • a third determining unit configured to determine a category name of the category of the product information in which the presentation order is less than the preset presentation order threshold, and determine the target push The name of the category of the category to which the information belongs
  • the matching unit is configured to match the determined category name of
  • each of the plurality of product information includes a product name
  • the target product information includes a target product name
  • the apparatus further includes: a fifth determining unit configured to display the plurality of product information Determining a similarity between the product name in the product information and the target product name in the target push information; and determining that the similarity is less than the preset similarity threshold, The product information is determined as the difference product information; the third adding unit is configured to increase the display order of the target push information by a third preset value in response to determining that the quantity of the difference product information is greater than the preset number threshold.
  • the apparatus further includes: a second extracting unit configured to extract first feature information from the preset first training sample, wherein the first training sample includes instructions for indicating corresponding to the first training sample The ordering identifier of the ordering situation; the first training unit is configured to utilize the machine learning algorithm to train the order rate prediction model based on the first feature information and the order identification.
  • the apparatus further includes: a third extracting unit configured to extract second feature information from the preset second training sample, wherein the second training sample includes an indication to correspond to the second training sample The click identification of the click condition; the second training unit is configured to utilize the machine learning algorithm to train the click rate prediction model based on the second feature information and the click identifier.
  • the information pushing method and apparatus by extracting a plurality of product information and a plurality of candidate push information that match the received search term, and then determining each product information and each candidate push information based on the order rate prediction model. Corresponding order rate, and determining a click rate corresponding to each candidate push information based on the click rate prediction model, and then determining target push information based on the obtained order rate and click rate, and finally pushing the target push information to the client, thereby realizing richness Targeted information push.
  • FIG. 1 is an exemplary system architecture diagram to which the present application can be applied;
  • FIG. 2 is a flow chart of one embodiment of an information push method according to the present application.
  • FIG. 3 is a schematic diagram of an application scenario of an information pushing method according to the present application.
  • FIG. 5 is a schematic structural diagram of an embodiment of an information pushing apparatus according to the present application.
  • FIG. 6 is a block diagram showing the structure of a computer system suitable for implementing the server of the embodiment of the present application.
  • FIG. 1 illustrates an exemplary system architecture 100 in which an information push method or information push device of the present application may be applied.
  • system architecture 100 can include terminal devices 101, 102, 103, network 104, and server 105.
  • the network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105.
  • Network 104 may include various types of connections, such as wired, wireless communication links, fiber optic cables, and the like.
  • the user can interact with the server 105 over the network 104 using the terminal devices 101, 102, 103 to receive or transmit messages and the like.
  • Various communication client applications such as a shopping application, a web browser application, a search application, an instant communication tool, a mailbox client, a social platform software, and the like, may be installed on the terminal devices 101, 102, and 103.
  • the terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop portable computers, desktop computers, and the like.
  • the server 105 may be a server that provides various services, such as a background management server that provides support to the shopping websites that the user browses with the terminal devices 101, 102, and 103.
  • the background management server may analyze and process data such as the received product information query request, and feed back the processing result (for example, target push information and product information) to the terminal device.
  • the webpage generating method provided by the embodiment of the present application is generally executed by the server 105. Accordingly, the webpage generating apparatus is generally disposed in the server 105.
  • terminal devices, networks, and servers in Figure 1 is merely illustrative. Depending on the implementation needs, there can be any number of terminal devices, networks, and servers.
  • the information pushing method includes the following steps:
  • Step 201 Receive a product information query request that is sent by a client and includes a search term.
  • the electronic device for example, the server shown in FIG. 1 on which the information pushing method runs can use the wired connection method or the wireless connection manner to use the terminal for querying the product information from the user (for example, as shown in FIG. 1 ).
  • the terminal device 101, 102, 103) receives the product information query request, wherein the product information query request may include a search term.
  • the above wireless connection manner may include but is not limited to 3G/4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods that are now known or developed in the future. .
  • the user can edit the search term in the interface presented by the shopping application installed by the client, and click the search button in the interface to send a product information query request.
  • the user can browse the shopping website by using a browser, edit the search term at the product search location of the webpage, and click the search button of the webpage page to send a product information query request.
  • the above webpage may include html format, xhtml format, asp format, php format, jsp format, shtml format, nsp format, xml format webpage or other webpages to be developed in the future (as long as the webpage file of this format can be used by the browser) Open and browse the forms it contains).
  • Step 202 Extract a plurality of product information and a plurality of candidate push information that match the search term.
  • a large amount of product information and candidate push information may be stored in the memory of the electronic device itself.
  • the electronic device may directly extract a plurality of product information and multiple matches that match the search term locally.
  • Candidate push information may also be stored in a remote server connected to the electronic device, and the electronic device may extract a plurality of product information matching the search term from the remote server.
  • the candidate push information may be stored in another server (for example, an advertisement server) connected to the electronic device, and the electronic device may extract a plurality of candidate push information matching the search term from the other server.
  • the product information of each product may include various information related to the product, for example, may include but not limited to product name, product image, product introduction, product link, product price, product monthly sales, and the like.
  • the candidate push information may include various information related to the candidate push product, and may include, but is not limited to, a candidate push product name, a candidate push product picture, a candidate push product profile, a candidate push product link, a candidate push product price, a candidate push product, and a candidate push product.
  • the electronic device may extract a plurality of product information and a plurality of candidate push information that match the search term in various manners.
  • the electronic device may first retrieve a product name in the product information that includes the search term; and then determine product information including the retrieved product name as the search. The word matches the product information and extracts the determined product information.
  • the electronic device may first search for a product name in the product information that includes the search term or the search term (for example, an English translation, an abbreviation of the search term, etc.). Thereafter, the product information including the retrieved product name is determined as the product information matching the above search term, and the determined product information is extracted.
  • the search term or the search term for example, an English translation, an abbreviation of the search term, etc.
  • the product information of each product may match a preset keyword set.
  • the above keyword set may include at least one keyword set in advance for describing the product.
  • a set of keywords that match the product information of the product may be set by the user who sold the product and uploaded to the electronic device.
  • the electronic device may match the search term with a keyword set corresponding to each product information. For each product information, if there is a keyword matching the search term in the keyword set corresponding to the product information, the electronic device may determine that the product information is product information that matches the search term, and extract the Determined product information.
  • the electronic device may extract candidate push information in the same manner as the product information is extracted. For example, the electronic device may first search for a candidate push product name of the candidate push information including the search term or the synonyms of the search term; and then, the candidate push information including the retrieved candidate push product name is determined as The above search words match the candidate push information, and extract the determined candidate push information. It should be noted that the manner of extracting the product information and the candidate push information may include, but is not limited to, the above enumeration, and details are not described herein again.
  • Step 203 Input each product information and each candidate push information into a pre-trained order rate prediction model, obtain an order rate corresponding to each product information and each candidate push information, and input each candidate push information to the pre-trained
  • the click rate prediction model obtains a click rate corresponding to each candidate push information.
  • the electronic device may input each product information and each candidate push information into a pre-trained order rate prediction model, and obtain an order rate corresponding to each product information and each candidate push information, and each candidate
  • the push information is input to the pre-trained click rate prediction model to obtain a click rate corresponding to each candidate push information.
  • the foregoing order rate prediction model may be used to represent the correspondence between the product information or the candidate push information and the order rate.
  • the click rate prediction model may be used to represent the correspondence between the candidate push information and the click rate.
  • the above-described order rate prediction model and the above-described click rate prediction model can be established in various ways.
  • a deep learning method may be adopted, which is established based on a deep neural network, may also be established based on a deep neural network and a convolutional neural network, and may also be jointly established based on a deep neural network, a convolutional neural network, and a cyclic neural network.
  • the order rate can refer to the click-through rate of the information, that is, the ratio of the actual number of clicks of the information to the amount of information displayed, which can be used to measure the effect of the information.
  • the click rate can refer to the ratio of the number of times a content on a website page is clicked to the number of times it is displayed, and can be used to characterize the degree of attention of the content.
  • the electronic device may pre-establish a ticket rate prediction model based on a learning ordering algorithm (for example, a Pairwise algorithm): first, the first training may be preset. The first feature information is extracted from the sample, where the first training sample includes a single order identifier for indicating an order situation corresponding to the first training sample, and the order identifier may be any character (number, letter, symbol) And so on) the composed string. It should be noted that the first training sample may be a sample selected based on a preset condition.
  • a learning ordering algorithm for example, a Pairwise algorithm
  • a plurality of product information is displayed in a certain history page, and if one of the two adjacent product information is placed, if the product corresponding to one of the product information is placed, and the product corresponding to the other product information is not When placed, you can treat the two product information as a first training sample. Thereafter, the feature information in the first training sample may be extracted based on the depth neural network, the convolutional neural network, and the cyclic neural network, and the extracted feature information may be determined as the first feature information; and finally, the machine learning algorithm may be utilized, based on the foregoing The first feature information and the above-mentioned order identification are trained to obtain the above-mentioned order rate prediction model.
  • the electronic device may pre-establish a click-rate prediction model based on a learning and sorting algorithm (for example, a Pairwise algorithm): first, a preset second training sample may be The first feature information is extracted, wherein the second training sample includes a click identifier for indicating a click situation corresponding to the second training sample, and the click identifier may be any character (number, letter, symbol, etc.) String. It should be noted that the second training sample may be a sample selected based on a preset condition.
  • a learning and sorting algorithm for example, a Pairwise algorithm
  • a plurality of product information is displayed in a certain history page, and if one of the two adjacent product information is clicked and another product information is not clicked, the two can be The product information is considered as a second training sample. Thereafter, the feature information in the second training sample may be extracted based on the deep neural network, the convolutional neural network, and the cyclic neural network, and the extracted feature information may be determined as the second feature information; finally, the machine learning algorithm may be utilized, based on the foregoing The second feature information and the click identifier are trained to obtain the above-mentioned click rate prediction model.
  • Step 204 Determine target push information in the plurality of candidate push information based on the obtained order rate and click rate.
  • the electronic device may determine the multiple candidate pushes by using various manners based on the obtained order rate corresponding to each product information and each candidate push information, and a click rate corresponding to each candidate push information.
  • Target candidate push information in the message may be determined by using various manners based on the obtained order rate corresponding to each product information and each candidate push information, and a click rate corresponding to each candidate push information.
  • the electronic device may first determine a billing rate corresponding to the product information as a target order rate; thereafter, Among the plurality of candidate push information, the candidate push information having a higher order rate than the target order rate is determined as the target candidate push information.
  • the electronic device may first determine a billing rate corresponding to the product information as a target order rate; thereafter, And extracting candidate push information that is higher than the target order rate in the plurality of candidate push information; and finally, candidate push information in the extracted candidate push information that has a click rate greater than a preset click rate preset Determine to push information as a target candidate.
  • the electronic device may first use the order rate corresponding to the product information as the order rate threshold, and select the multiple Among the candidate push information, the candidate push information having a higher order rate than the above-mentioned order rate threshold is generated, and a candidate push information set matching the product information is generated. Then, for each of the generated candidate push information sets, a first expected value of each candidate push information in the candidate push information set may be determined, where the first expected value of each candidate push information may be corresponding to the candidate push information. The click rate and the preset product of the billing value corresponding to the candidate push information. In practice, the charging value corresponding to each candidate push information may be the transaction price of the bid of the candidate push information. Finally, the electronic device may determine target push information in the plurality of candidate push information in various manners based on the obtained first expected value.
  • the preset electronic device may be pre-stored with a preset first expected value, and based on the obtained first expected value, determining that the target push information in the multiple candidate push information may be The electronic device may determine, as the target push information, the candidate push information in the set of candidate push information that the first expected value is greater than the preset first expected value.
  • the preset electronic device may be pre-stored with a preset first expected value corresponding to each product information, and the determining the multiple candidate push information based on the obtained first expected value.
  • the target push information may be performed as follows: for each generated candidate push information set, the electronic device may first determine product information corresponding to the candidate push information set; and thereafter, obtain corresponding to the determined product information. And preset the first expected value; and finally, the candidate push information in the candidate push information set that the first expected value is greater than the preset first expected value is determined as the target push information.
  • each of the plurality of product information may be provided with a presentation order identifier for indicating a presentation order of the product information, and a candidate push information set corresponding to the product information.
  • Each of the candidate push information in the pair has the above-described presentation order identifier.
  • the above presentation order identifiers may be numbers, such as 1, 2, and the like.
  • the electronic device may acquire a preset order coefficient corresponding to the presentation order indicated by the presentation order identifier carried by the target candidate push information, And determining a second expected value of the target candidate push information, wherein the second expected value is a product of a first expected value of the target candidate push information and a sequence coefficient corresponding to the target candidate push information.
  • the order coefficients corresponding to the presentation order indicated by the respective presentation order identifiers may be predetermined and stored by the technician in the electronic device based on a large number of statistical calculations.
  • the electronic device may determine the target candidate push information having the second expected value as the target push information.
  • Step 205 Push the target push information and the plurality of product information to the client.
  • the electronic device may push the plurality of product information extracted in step 202 and the target push information determined in step 204 to the client.
  • FIG. 3 is a schematic diagram 300 of an application scenario of the information pushing method according to the embodiment.
  • the client 301 sends a product information query request 303 containing a search term to the server 302.
  • the server 302 extracts a plurality of product information 304 and a plurality of candidate push information 305 that match the search term.
  • the server 302 inputs the product information and the candidate push information to the order rate prediction model, and inputs the candidate push information to the click rate prediction model to obtain the order rate 306 and the click rate 307.
  • the server 302 determines the target push information 308 of the plurality of candidate push information 305 based on the order rate 306 and the click rate 307. Finally, the server 302 pushes the target push information 308 and the plurality of product information 304 to the client 301.
  • the foregoing embodiment of the present application provides a method for extracting a plurality of product information and a plurality of candidate push information that match a received search term, and then determining each product information and each candidate push information corresponding to the next order rate prediction model.
  • Single rate and based on the click rate prediction model, determine the click rate corresponding to each candidate push information, and then determine the target push information based on the obtained order rate and click rate, and finally push the target push information to the client, achieving a targeted Information push.
  • the flow 400 of the information pushing method includes the following steps:
  • Step 401 Receive a product information query request that is sent by a client and includes a search term.
  • the electronic device for example, the server shown in FIG. 1 on which the information pushing method runs can use the wired connection method or the wireless connection manner to use the terminal for querying the product information from the user (for example, as shown in FIG. 1 ).
  • the terminal device 101, 102, 103) receives the product information query request, wherein the product information query request may include a search term.
  • Step 402 Extract a plurality of product information and a plurality of candidate push information that match the search term.
  • a large amount of product information and candidate push information may be stored in the memory of the electronic device itself.
  • the electronic device may directly extract a plurality of product information and multiple matches that match the search term locally.
  • Candidate push information may be stored in the memory of the electronic device itself.
  • each of the plurality of product information may have a presentation order identifier for indicating a presentation order of the product information
  • the presentation order identifier may be a number, for example, 1, 2, or the like.
  • Step 403 Input each product information and each candidate push information into a pre-trained order rate prediction model, obtain an order rate corresponding to each product information and each candidate push information, and input each candidate push information to the pre-trained
  • the click rate prediction model obtains a click rate corresponding to each candidate push information.
  • the electronic device may input each product information and each candidate push information into a pre-trained order rate prediction model, and obtain an order rate corresponding to each product information and each candidate push information, and each candidate
  • the push information is input to the pre-trained click rate prediction model to obtain a click rate corresponding to each candidate push information.
  • the foregoing order rate prediction model may be used to represent the correspondence between the product information or the candidate push information and the order rate.
  • the click rate prediction model may be used to represent the correspondence between the candidate push information and the click rate.
  • Step 404 For each product information of the plurality of product information, using a billing rate corresponding to the product information as a billing rate threshold, selecting a candidate of the plurality of candidate push information that the order rate is greater than a billing rate threshold Push information to generate a set of candidate push information that matches the product information.
  • the electronic device may use an order rate corresponding to the product information as a billing rate threshold, and select one of the plurality of candidate push information.
  • the candidate push information having a single rate greater than the above-mentioned order rate threshold generates a candidate push information set that matches the product information.
  • each candidate push information in the candidate push information set corresponding to the product information may be the same as the presentation order identifier carried by the product information. Show order identification.
  • Step 405 Determine, for each generated candidate push information set, a first expected value of each candidate push information in the candidate push information set.
  • the electronic device may determine a first expected value of each candidate push information in the candidate push information set, where the first candidate of each candidate push information
  • the expected value may be a product of a click rate corresponding to the candidate push information and a preset billing value corresponding to the candidate push information.
  • the charging value corresponding to each candidate push information may be the transaction price of the bid of the candidate push information.
  • Step 406 The candidate push information having the largest first expected value among the candidate push information sets is used as the target candidate push information, and the target candidate push information set is generated.
  • the electronic device may generate the target candidate push information set by using the candidate push information having the largest first expected value in each candidate push information set generated in step 405 as the target candidate push information.
  • Step 407 For each target candidate push information in the target candidate push information set, acquire a preset order coefficient corresponding to the presentation order indicated by the presentation order identifier carried by the target candidate push information, and determine the target. The second expected value of the candidate push information.
  • the electronic device may acquire a preset indication, which is indicated by the presentation order identifier of the target candidate push information.
  • the order coefficient corresponding to the order is presented, and a second expected value of the target candidate push information is determined.
  • the second expected value may be a product of a first expected value of the target candidate push information and a sequence coefficient corresponding to the target candidate push information.
  • the order coefficients corresponding to the presentation order indicated by the respective presentation order identifiers may be predetermined and stored by the technician in the electronic device based on a large number of statistical calculations. Generally, the lower the presentation order, the larger the order coefficient corresponding to the presentation order.
  • Step 408 Determine target candidate push information with the second expected value as the target push information.
  • the electronic device may determine the target candidate push information having the second highest expected value among the target candidate push information sets as the target push information.
  • Step 409 determining a presentation order indicated by the presentation order identifier carried by the target push information, and determining the determined presentation order as the target presentation order.
  • the electronic device may determine a presentation order indicated by the presentation order identifier carried by the target push information, and determine the determined presentation order as the target presentation order.
  • Step 410 Determine a presentation order indicated by a presentation order identifier carried by each product information in the plurality of product information, and increase a presentation order of the product information to each product information in which the presentation order is not less than the target presentation order. Preset value.
  • the electronic device may determine a presentation order indicated by a presentation order identifier carried by each of the plurality of product information, and for each product information whose presentation order is not less than the target presentation order, The presentation order of the product information is increased by a first preset value (for example, 1). As an example, if the target presentation order is 4, the electronic device may add 1 to the presentation order of the product information whose presentation order is not less than 4 (for example, 4, 5, 6, etc.), that is, the product information whose original presentation order is 4. The new presentation order is 5.
  • step 411 the plurality of product information and the target product information are sorted in order of presentation order from small to large.
  • the electronic device may sort the plurality of product information and the target product information in an order of presentation from small to large.
  • the electronic device may further perform the following steps: First, it may be determined a category name (for example, "clothing", “electric appliance”, etc.) of the category of the product information in which the presentation order is smaller than the preset presentation order threshold, and the category of the category to which the target push information belongs is determined in the plurality of product information. name. Thereafter, the electronic device may match the determined category name of the category to which the respective product information belongs to the category name of the category to which the target push information belongs.
  • a category name for example, "clothing", "electric appliance”, etc.
  • the electronic device may increase the presentation order of the target push information by a second predetermined value (eg, 1, 2, etc.).
  • a second predetermined value eg, 1, 2, etc.
  • the electronic device may further perform the following steps: In the product information, each of the product information in which the presentation order is smaller than the preset presentation order threshold, the electronic device may determine the similarity between the product name in the product information and the target product name in the target push information. Then, in response to the determined similarity being less than the preset similarity threshold, the product information may be determined as the difference product information. Finally, in response to determining that the number of the difference product information is greater than the preset number threshold, the electronic device may increase the presentation order of the target push information by a third preset value (eg, 1, 2, etc.).
  • a third preset value eg, 1, 2, etc.
  • Step 412 Generate a webpage including the sorted product information and the target product information.
  • the electronic device may generate a webpage including the sorted plurality of product information and the target product information.
  • Step 413 sending a webpage to the client.
  • the electronic device may send the webpage to the client.
  • the flow 400 of the information push method in the present embodiment highlights the step of determining the presentation order of the target push information and the plurality of product information as compared with the embodiment corresponding to Fig. 2. Therefore, the solution described in this embodiment can dynamically determine the presentation position of the target push information, so that the user can find the information of interest more quickly, and realize the information-pushing information while realizing the targeted information push. flexibility.
  • the present application provides an embodiment of an information pushing apparatus, and the apparatus embodiment corresponds to the method embodiment shown in FIG. Used in a variety of electronic devices.
  • the information pushing apparatus 500 of the present embodiment includes: a receiving unit 501 configured to receive a product information query request including a search term sent by a client; and a first extracting unit 502 configured to extract and The plurality of product information and the plurality of candidate push information matched by the search term; the input unit 503 is configured to input each product information and each candidate push information into a pre-trained order rate prediction model, and obtain the product information and a billing rate corresponding to each candidate push information, and inputting each candidate push information to a pre-trained click rate prediction model to obtain a click rate corresponding to each candidate push information, wherein the order rate prediction model is used to represent product information Or the corresponding relationship between the candidate push information and the order rate, the click rate prediction model is used to represent the correspondence between the candidate push information and the click rate; the first determining unit 504 is configured to use the obtained order rate and the click rate, Determining target push information in the plurality of candidate push information; pushing unit 505 configured to use the above target The push information and the
  • the receiving unit 501 can receive a product information query request from a terminal (for example, the terminal device 101, 102, and 103 shown in FIG. 1) that the user uses to perform product information query by using a wired connection manner or a wireless connection manner.
  • the product information query request may include a search term.
  • the first extracting unit 502 may extract a plurality of product information and a plurality of candidate push information that match the search term in various manners.
  • the input unit 503 may input each product information and each candidate push information into a pre-trained order rate prediction model, and obtain an order rate corresponding to each product information and each candidate push information, and each The candidate push information is input to the pre-trained click rate prediction model to obtain a click rate corresponding to each candidate push information.
  • the first determining unit 504 may determine the foregoing by using various manners based on the obtained order rate corresponding to each product information and each candidate push information, and the click rate corresponding to each candidate push information.
  • Target candidate push information in the candidate push information may be determined.
  • the determining unit 504 may include a first generating module, a first determining module, and a second determining module (not shown).
  • the first generation module may be configured to use, for each product information of the plurality of product information, a billing rate corresponding to the product information as a billing rate threshold, and select one of the plurality of candidate push information.
  • the candidate push information with the order rate greater than the above-mentioned order rate threshold is generated, and a candidate push information set matching the product information is generated.
  • the first determining module may be configured to determine, for each generated candidate push information set, a first expected value of each candidate push information in the candidate push information set, where a first expected value of each candidate push information is The click rate corresponding to the candidate push information is a product of a preset billing value corresponding to the candidate push information.
  • the second determining module may be configured to determine target push information in the plurality of candidate push information based on the obtained first expected value.
  • each of the plurality of product information may be provided with a presentation order identifier for indicating a presentation order of the product information, and a candidate push information set corresponding to the product information.
  • Each of the candidate push information in the present may be tagged with the above-described presentation order.
  • the foregoing second determining module may include a generating submodule, a first determining submodule, and a second determining submodule (not shown).
  • the generation sub-module may be configured to use the candidate push information having the largest first expected value among the candidate push information sets as the target candidate push information to generate the target candidate push information set.
  • the foregoing first determining sub-module may be configured to: for each target candidate push information in the target candidate push information set, obtain a preset, corresponding to a display order indicated by the display order identifier of the target candidate push information.
  • the second determining submodule may be configured to determine the target candidate push information with the second expected value as the target push information.
  • the information pushing apparatus 500 may further include a second determining unit and a first adding unit (not shown).
  • the second determining unit may be configured to determine a presentation order indicated by the presentation order identifier carried by the target push information, and determine the determined presentation order as the target presentation order.
  • the first adding unit may be configured to determine a presentation order indicated by a presentation order identifier carried by each of the plurality of product information, and for each product information whose presentation order is not less than the target presentation order, The presentation order of the product information is increased by the first preset value.
  • the information pushing apparatus 500 may further include a third determining unit, a matching unit, a fourth determining unit, and a second adding unit (not shown).
  • the third determining unit may be configured to determine a category name of the category of the product information in which the presentation order is less than the preset presentation order threshold, and determine the category of the category to which the target push information belongs.
  • the matching unit may be configured to match the determined category name of the category to which the respective product information belongs to the category name of the category to which the target push information belongs.
  • the fourth determining unit may be configured to determine the number of product information whose category name matches the category name of the category to which the target push information belongs, and determine the total number of the plurality of product information.
  • the second adding unit may be configured to increase the display order of the target pushing information by a second preset value in response to determining that the ratio of the quantity to the total quantity is less than a preset ratio.
  • each of the plurality of product information includes a product name
  • the target product information includes a target product name
  • the information pushing device 500 further includes a fifth determining unit and The third addition unit (not shown).
  • the fifth determining unit may be configured to determine, for each product information of the plurality of product information that the presentation order is less than the preset presentation order threshold, the product name in the product information and the target in the target push information.
  • the third adding unit may be configured to increase the display order of the target push information by a third preset value in response to determining that the quantity of the difference product information is greater than a preset number threshold.
  • the pushing unit 505 can push the plurality of product information and the target push information to the client.
  • the pushing unit 505 may include a sorting module, a second generating module, and a sending module (not shown).
  • the sorting module may be configured to sort the plurality of product information and the target product information in an order of presentation from small to large.
  • the second generation module may be configured to generate a webpage including the sorted plurality of product information and the target product information.
  • the sending module may be configured to send the webpage to the client.
  • the information pushing apparatus 500 may further include a second extracting unit and a first training unit (not shown).
  • the second extraction unit may be configured to extract the first feature information from the preset first training sample, where the first training sample includes an order for indicating an order corresponding to the first training sample.
  • Single logo The first training unit may be configured to use the machine learning algorithm to train the foregoing order rate prediction model based on the first feature information and the order identifier.
  • the information pushing apparatus 500 may further include a third extracting unit and a second training unit (not shown).
  • the third extraction unit may be configured to extract second feature information from the preset second training sample, where the second training sample includes a click identifier for indicating a click situation corresponding to the second training sample.
  • the second training unit may be configured to use the machine learning algorithm to train the click rate prediction model based on the second feature information and the click identifier.
  • the apparatus extracts a plurality of product information and a plurality of candidate push information that match the search term received by the receiving unit 501 to the first extracting unit 502, and then the input unit 503 predicts the order rate based on the order rate.
  • the model determines the order rate corresponding to each product information and each candidate push information, and determines the click rate corresponding to each candidate push information based on the click rate prediction model, and then the first determining unit 504 determines the target push based on the obtained order rate and the click rate.
  • the information, the final push unit 505 pushes the target push information to the client, and implements targeted information push.
  • FIG. 6 a block diagram of a computer system 600 suitable for use in implementing a server of an embodiment of the present application is shown.
  • the server shown in FIG. 6 is merely an example, and should not impose any limitation on the function and scope of use of the embodiments of the present application.
  • computer system 600 includes a central processing unit (CPU) 601 that can be loaded into a program in random access memory (RAM) 603 according to a program stored in read only memory (ROM) 602 or from storage portion 608. And perform various appropriate actions and processes.
  • RAM random access memory
  • ROM read only memory
  • RAM random access memory
  • various programs and data required for the operation of the system 600 are also stored.
  • the CPU 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604.
  • An input/output (I/O) interface 605 is also coupled to bus 604.
  • the following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a storage portion 608 including a hard disk or the like. And a communication portion 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the Internet.
  • Driver 610 is also coupled to I/O interface 605 as needed.
  • a removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory or the like, is mounted on the drive 610 as needed so that a computer program read therefrom is installed into the storage portion 608 as needed.
  • an embodiment of the present disclosure includes a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for executing the method illustrated in the flowchart.
  • the computer program can be downloaded and installed from the network via communication portion 609, and/or installed from removable media 611.
  • the central processing unit (CPU) 601 the above-described functions defined in the method of the present application are performed.
  • the computer readable medium described herein may be a computer readable signal medium or a computer readable storage medium or any combination of the two.
  • the computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.
  • a computer readable storage medium may be any tangible medium that can contain or store a program, which can be used by or in connection with an instruction execution system, apparatus or device.
  • a computer readable signal medium may include a data signal that is propagated in the baseband or as part of a carrier, carrying computer readable program code. Such propagated data signals can take a variety of forms including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer readable signal medium can also be any computer readable medium other than a computer readable storage medium, which can transmit, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device.
  • Program code embodied on a computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
  • each block of the flowchart or block diagram can represent a module, a program segment, or a portion of code that includes one or more of the logic functions for implementing the specified.
  • Executable instructions can also occur in a different order than that illustrated in the drawings. For example, two successively represented blocks may in fact be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented in a dedicated hardware-based system that performs the specified function or operation. Or it can be implemented by a combination of dedicated hardware and computer instructions.
  • the units involved in the embodiments of the present application may be implemented by software or by hardware.
  • the described unit may also be provided in the processor, for example, as a processor including a receiving unit, a first extracting unit, an input unit, a first determining unit, and a pushing unit.
  • the names of these units do not constitute a limitation on the unit itself under certain circumstances.
  • the receiving unit may also be described as "a unit that receives a product information inquiry request".
  • the present application also provides a computer readable medium, which may be included in the apparatus described in the above embodiments, or may be separately present and not incorporated into the apparatus.
  • the computer readable medium carries one or more programs, when the one or more programs are executed by the device, causing the device to: receive a product information query request sent by a client and including a search term; and extract the search term Matching a plurality of product information and a plurality of candidate push information; inputting each product information and each candidate push information into a pre-trained order rate prediction model, and obtaining an order rate corresponding to each product information and each candidate push information, and Inputting each candidate push information into a pre-trained click rate prediction model to obtain a click rate corresponding to each candidate push information; and determining target push information in the plurality of candidate push information based on the obtained order rate and click rate; Pushing the target push information and the plurality of product information to the client.

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Abstract

La présente invention concerne un procédé et un dispositif de campagne d'informations. Un mode de réalisation du procédé consiste : à recevoir une demande d'interrogation d'informations de produit contenant un mot de recherche, envoyée par un client ; à extraire de multiples éléments d'informations de produit et de multiples éléments d'informations candidates à promouvoir, lesdites informations correspondant au mot de recherche ; à entrer les éléments d'informations de produit et les éléments d'informations candidates à promouvoir dans un modèle de prédiction de taux de placement d'ordre précédemment appris de sorte à obtenir des taux de placement d'ordre correspondant aux éléments d'informations de produit et aux éléments d'informations candidates à promouvoir, et à entrer les éléments d'informations candidates à promouvoir dans un modèle de prédiction de taux de clics formés auparavant de sorte à obtenir des vitesses de clics correspondant aux éléments d'informations candidates à promouvoir ; à déterminer, sur la base des taux de placement d'ordre obtenus et des taux de clics, des informations cibles à promouvoir parmi les éléments d'informations candidates à promouvoir ; et à promouvoir au client les informations cibles à promouvoir et les multiples éléments d'informations de produit. Le mode de réalisation de l'application permet une campagne d'informations spécifique.
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