CN115238163A - Information pushing method and device based on document data, storage medium and terminal - Google Patents

Abstract

A document data-based information push method, device, storage medium, and terminal, the document data-based information push method comprises: crawling document data published by multiple users, the document data including document subject and citation document data; if different document data If the author has the same name, at least the author with the same name is identified according to the subject similarity of the document data; according to the crawled document data, the document data published by at least some of the multiple users and the cited document data are extracted. The published document data includes: The number of published documents, the cited document data includes the number of cited documents; the evaluation results of at least some users are calculated based on the literature data published by at least some users and the cited document data; the information is pushed according to the evaluation results of each user, and the pushed information includes the user and/or user bibliographic data. The technical solution of the present invention can truly and accurately realize information push based on document data.

Description

Document data-based information push method and device, storage medium and terminal

technical field

The present invention relates to the technical field of data processing, and in particular, to a method and device for pushing information based on document data, a storage medium and a terminal.

Background technique

The H index is a hybrid quantitative indicator that can be used to assess the amount and level of academic output of researchers. The user's H-index means that among the Np papers published by the user, each of H papers is cited at least H times, and each of the remaining Np-H papers is cited less than or equal to H times.

However, the H-index in the prior art is not sensitive to the published data and highly cited data of talents, so that the use of the H-index to evaluate and recommend talents cannot reflect the real situation.

SUMMARY OF THE INVENTION

The technical problem solved by the present invention is how to truly and accurately realize information push based on document data.

In order to solve the above technical problem, an embodiment of the present invention provides an information push method based on a multi-layer citation network. The information push method based on the multi-layer citation network includes: crawling document data published by multiple users, where the document data includes documents Subject and cited document data; if the authors of different document data have the same name, at least identify the author with the same name according to the subject similarity of the document data; extract the articles published by at least some of the multiple users according to the crawled document data Document data and cited document data, the published document data includes the number of published documents, and the cited document data includes the number of cited documents; calculating the document data at least according to the document data published by the at least some users and the cited document data Evaluation results of at least some users; information is pushed according to the evaluation results of each user, and the pushed information includes the user and/or the user's document data.

Optionally, the calculating at least the evaluation result of the at least some users according to the document data published by the at least some users and the cited document data includes: calculating the total number of cited documents by using the cited document data; determining the Calculate the number of zero-cited documents in the published document data, and calculate the weighted sum of the number of zero-cited documents and the number of remaining documents; calculate the evaluation of the at least some users according to the total number of cited documents and the weighted sum result.

Optionally, the calculating at least the evaluation result of the at least some users according to the document data published by the at least some users and the cited document data includes: calculating the total number of cited documents by using the cited document data; determining the The number of zero-cited documents in the published document data, and calculate the weighted sum of the number of zero-cited documents and the number of remaining documents; calculate the H-index of the at least some users; compare the H-index with the total number of cited documents; The amount and the weighted sum are combined to obtain the evaluation result of the at least part of the users.

Optionally, the determining of the document data published by the at least some users and the cited document data according to the crawled multiple documents includes: establishing a published document database according to the crawled multiple documents, and the published document database includes various documents. Document data published by users; establish a citation database based on multiple crawled documents, the citation database includes document data cited by the document data published by each user; determine the The number of literature data published by at least some users and the number of citations.

Optionally, after the crawling of document data published by multiple users includes: standardizing the authors and issuing agencies in the document data according to a preset format; and/or, if the authors of different document data are authors of the same name , the author of the same name is identified at least according to the subject similarity of the document data.

Optionally, the identifying the author at least according to the subject similarity of the document data includes: calculating the subject similarity between the document data; if the subject similarity is less than a first preset threshold, determining the same name. The proportion of the documents published by the author appearing in the same name institution within the same time period; if the proportion is greater than the second preset threshold, it is determined that the documents published by the author with the same name appear in the same time period and the co-authors in the same institution. ratio; if the ratio is greater than the third preset threshold, determine that the author with the same name is the same author; otherwise, determine that the author with the same name is a different author.

Optionally, the document data includes title, year, source, institution, keywords, and abstract; the types of documents include papers, patents, books, and conference reports.

In order to solve the above technical problems, the embodiment of the present invention also discloses an information push device based on document data. The information push device based on document data includes: a document data crawling module for crawling document data published by multiple users, The document data includes document subject and cited document data; the same name identification module is used to identify the author with the same name at least according to the subject similarity of the document data if the authors of different document data have the same name; the document data determination module is used to identify the author according to the same name. The crawled multiple document data determines the document data published by the at least some users and the cited document data, the published document data includes the number of published documents, and the cited document data includes the number of cited documents; evaluation result calculation module is used to calculate the evaluation results of the at least part of the users according to the literature data published by the at least part of the users and the cited literature data; the push module is used to push information according to the evaluation results of each user, and the pushed information includes the user and/or bibliographic data of the user.

The embodiment of the present invention further discloses a storage medium on which a computer program is stored, and when the computer program is run by a processor, the steps of the method for pushing information based on document data are executed.

An embodiment of the present invention further discloses a terminal, including a memory and a processor, the memory stores a computer program that can be run on the processor, and the processor executes the document-based document when running the computer program. The steps of the information push method of the data.

Compared with the prior art, the technical solutions of the embodiments of the present invention have the following beneficial effects:

In the technical solution of the present invention, the evaluation result of the at least part of the users is calculated by using the document data published by at least some users and the cited document data, the published document data includes the number of published documents, and the cited document data includes The number of cited documents and used for information push; compared with the H index in the prior art, the calculated user evaluation results can reflect the user's published document volume and citation frequency, so that the evaluation results can reflect the user's published document data and highly cited data. . In addition, by distinguishing the authors of the same name in the literature, the accuracy of the literature data can be improved, the real situation of the user can be more truly reflected, and the accuracy of information push (such as talent recommendation) can be improved.

Description of drawings

Fig. 1 is a flow chart of a method for pushing information based on document data according to an embodiment of the present invention;

FIG. 2 is a flowchart of a specific implementation of step S103 shown in FIG. 1;

FIG. 3 is a schematic structural diagram of an apparatus for pushing information based on document data according to an embodiment of the present invention.

Detailed ways

As described in the background art, the H-index in the prior art is not sensitive to the published data and highly cited data of talents, so that the H-index for talent evaluation and talent recommendation cannot reflect the real situation.

In the technical solution of the present invention, the evaluation result of the at least part of the users is calculated by using the document data published by at least some users and the cited document data, the published document data includes the number of published documents, and the cited document data includes The number of cited documents and used for information push; compared with the H index in the prior art, the calculated user evaluation results can reflect the user's published document volume and citation frequency, so that the evaluation results can reflect the user's published document data and highly cited data. . In addition, by distinguishing the authors of the same name in the literature, the accuracy of the literature data can be improved, the real situation of the user can be more truly reflected, and the accuracy of information push (such as talent recommendation) can be improved.

In order to make the above objects, features and advantages of the present invention more clearly understood, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

FIG. 1 is a flowchart of a method for pushing information based on document data according to an embodiment of the present invention.

The method for pushing information based on document data in this embodiment of the present invention may be performed by a computing device, and the computing device may be various appropriate terminals, such as a mobile phone, a computer, an Internet of Things device, a server, etc., but is not limited thereto.

The documents referred to in the embodiments of the present invention may be papers, or may be any document data with references, such as patents, books, and conference reports, which are not limited in the embodiments of the present invention.

Specifically, the method for pushing information based on document data may include the following steps:

Step S101: Crawling document data published by multiple users, where the document data includes document topics and cited document data;

Step S102: If the authors of different document data have the same name, then at least identify the author with the same name according to the subject similarity of the document data;

Step S103: Extracting document data and citation document data published by at least some of the multiple users according to the crawled document data, where the published document data includes the number of published documents, and the citation document data includes citing documents quantity;

Step S104: Calculate the evaluation result of the at least some users according to at least the document data published by the at least some users and the cited document data;

Step S105: Push information according to the evaluation results of each user, and the pushed information includes the user and/or the user's document data.

It should be noted that the sequence numbers of the steps in this embodiment do not represent limitations on the execution order of the steps.

The evaluation result in this embodiment may be used to evaluate the academic output quantity and academic output level of the user, and may be called the H++ index, or any other implementable name, which is not limited in this embodiment of the present invention.

In the specific implementation of step S101, document data of multiple users may be obtained by crawling, and specifically, the document data may be collected from multiple document source databases by using a crawler technology. In a specific application scenario, the multiple users may be users belonging to the same discipline. Then, by calculating the evaluation results of multiple users in the subsequent steps, users with higher academic levels in the subject can be determined for talent recommendation.

Specifically, the crawled document data may include a title, a year, a source, an organization, a keyword, and an abstract, or other public information, which is not limited in this embodiment of the present invention.

It should be noted that after crawling the document data, operations such as deduplication and standardization can also be performed on the crawled document data. For the specific implementation of the crawler technology and data cleaning, reference may be made to the prior art, which is not limited in this embodiment of the present invention.

Since the names of authors of different documents may be the same, but they do not refer to the same person, it is necessary to distinguish authors with the same name to avoid statistical errors in the case of indiscriminate data and to ensure the accuracy of subsequent calculations. In the specific implementation of step S102, whether the authors with the same name are the same person may be determined at least according to the subject similarity of the documents. Topic similarity can be calculated based on titles, keywords and abstracts between documents, and natural language processing algorithms can be used for specific calculations, such as Chinese Lexical Analysis System (Institute of Computing Technology, Chinese Lexical Analysis System, ICTCLAS), jieba word segmentation and term frequency-inverse document frequency (TF-IDF) algorithm, etc., which are not limited in this embodiment of the present invention.

If the subject similarity of the two documents reaches a preset threshold, for example, 60%, it can be determined that the two articles are articles in the same field, and it can be verified that the authors with the same name are the same person. If the topic similarity is found to be lower than a preset threshold, it is considered that the author with the same name is not the same person.

After the data cleaning in step S102, the crawled document data has higher accuracy, which lays a foundation for the accuracy of subsequent evaluation results calculation.

In the specific implementation of step S103-step S104, for at least some users, the literature data published by them can be determined, such as the number, title, year, source, organization, keywords and abstract of the literature published by at least part of the users; and at least The citation data of the articles published by some users, such as the number of citations, the title, year, source, institution, keywords, and abstract of the cited articles. When calculating the evaluation results of at least some users, at least the number of published documents and the number of cited documents can be used for calculation. In this way, the user's post volume and citation frequency can be reflected in the user's evaluation result.

Specifically, the evaluation results may be in the form of numerical values, such as scores, or may be in the form of grades. The evaluation results are positively correlated with the number of published documents and the number of cited documents. The more documents a user publishes, the higher the user's evaluation result is; the more the user's cited documents are, the higher the user's evaluation result is.

The evaluation result in the embodiment of the present invention can reflect the amount of documents posted by the user, and can also reflect the amount of citations. For users who publish few papers but have high number of citations, the embodiment of the present invention can obtain higher evaluation results, thereby solving the problems caused by the unscientific evaluation of talents who do unpopular research and the citation lag in the prior art .

Furthermore, in the specific implementation of step S105, information is pushed to the user through the user's evaluation result, which can improve the accuracy of the talent recommendation.

In a specific embodiment of the present invention, the calculated evaluation results of each user may be stored to form a talent database. The talent database includes the identifiers of each user and their corresponding evaluation results. Further, the data in the talent database can also be updated regularly.

In a non-limiting embodiment of the present invention, step S104 shown in FIG. 1 may include the following steps: calculating the total number of cited documents using the cited document data; determining the number of zero-cited documents in the published document data, and Calculate the weighted sum of the number of zero-cited documents and the number of remaining documents; calculate the evaluation result of the at least some users according to the total amount of cited documents and the sum.

In this embodiment, there are zero-cited documents in the documents published by the user, that is, there are no cited documents in the document; the impact of the zero-cited documents on the user's evaluation results is relatively small, so the number of the documents can be compared with the number of the remaining documents. The weighted calculation of the evaluation results to reduce the impact of zero-cited literature on the user's evaluation results. The weighted sum can reflect the number of articles published by the user.

In addition, the total number of citations can also be calculated, that is, for all the documents published by the user to be rated, the total number of citations can be obtained by summing the number of citations. The user's evaluation result can be calculated based on the total number of citations and the above weighted sum.

It should be noted that, any implementable mathematical operation method may be used to calculate the total number of cited documents and the above weighted sum to obtain the user's evaluation result. For example, the total number of citations and the above sum can be directly summed or multiplied; or, if the total number of citations is relatively large, in order to avoid excessive influence on the evaluation results, the square root, cube root, logarithm, etc. can be calculated. The operation, on this basis, is then added to the above sum, multiplied, and the like, which is not limited in this embodiment of the present invention.

In another non-limiting embodiment of the present invention, step S104 shown in FIG. 1 may include the following steps: calculating the total number of cited documents by using the cited document data; determining the number of zero-cited documents in the published document data, and calculate the weighted sum of the number of zero-cited documents and the number of remaining documents; calculate the H-index of the at least part of the users; combine the H-index with the total number of cited documents and the sum to obtain the Describe the evaluation results of at least some users.

Different from the foregoing embodiments, in the embodiment of the present invention, in addition to calculating the total number of cited documents and the above weighted sum, the H-index of at least some users is also calculated. The evaluation results of at least some users are calculated based on the H-index, the total number of citations, and the above weighted sum.

It should be noted that the H-index, the total number of cited documents, and the above weighted sum can be calculated by using any implementable mathematical operation method to obtain the user's evaluation result. For a specific implementation manner of calculating the H index, reference may be made to the prior art, and details are not described herein again in this embodiment of the present invention.

In a specific embodiment of the present invention, please refer to FIG. 2. Step S103 shown in FIG. 1 may include the following steps:

Step S201: establishing a publication database according to the crawled multiple documents, where the publication database includes document data published by each user;

Step S202: establishing a citation database according to the crawled multiple documents, the citation database including the document data cited by the document data published by each user;

Step S203: Determine the number of document data and the number of cited documents published by the at least some users according to the publication database and the citation database.

In this embodiment, a plurality of crawled documents may form a talent document database, such as a talent paper database. On this basis, the data in the talent literature database can be split to form a publication database and a citation database. The publication database is mainly used to store all the literature data published by talents (including title, publication year, source journal, affiliation, keywords, abstracts, etc.), and the citation database is mainly used to store the data of other literatures cited in the literature published by talents , and then form the data of talents citing other people, which in turn can filter out the citing people and the number of citations for the specified talents.

Specifically, when determining the document data published by at least some users, the subsequent steps may be determined based on the data in the publication database; when determining the cited document data of at least some users, it may be determined based on the data in the citation database. .

In a non-limiting embodiment of the present invention, after step S101 shown in FIG. 1 , the following steps may be included: standardize the author and the issuing institution in the document data according to a preset format.

In the embodiment of the present invention, the crawled data can be cleaned. Authors and issuing agencies in the document data may be standardized according to a preset format. For example, the formatting processing of different abbreviation methods in the literature and the standardization processing of the description of the same author, the distinction and processing of different papers of the same author, and the standardization processing of the description methods of different institutions in the papers.

Furthermore, when distinguishing between different papers by authors with the same name, the following steps can be used: calculating the subject similarity between document data; if the subject similarity is less than a first preset threshold, determine the publication by the author with the same name. The proportion of the literature with the same name in the same time period; if the proportion is greater than the second preset threshold, determine the proportion of the literature published by the same name author in the same time period and the same organization; if the proportion is greater than the second preset threshold If the ratio is greater than the third preset threshold, it is determined that the author with the same name is the same author; otherwise, it is determined that the author with the same name is a different author.

In the specific implementation, due to the different ways of writing the author's name, for example, the surname comes after the first name, the first name comes after the surname, and the name is abbreviated. Therefore, in this embodiment, the initial identification of the author's name can be performed in advance, and then the author with the same name can be identified. deduplication processing. First, extract author information from all documents, sort all authors, and merge authors with the same name in the scanning sequence for the first time to form a preliminary author set. Then, arrange the remaining authors in alphabetical order by author names, and compare them in pairs. If the two strings completely match the order of the shorter strings, it is considered that the two names may be the same person; in order to further To confirm, it is necessary to extract the "last name" and "first name" in the two original names, and compare them according to the different arrangements of the first and last names (the comparison method is the same as above, but the alphabetical order is not adjusted). If all the arrangements are traversed, and there is a shortest matching arrangement (for example: Thomas Huang and T. Huang), then it is further confirmed that the two names are suspected to be the same person, and the second merge is performed and placed in the preliminary author set middle. A second merge was made and added to the preliminary author set.

In the preliminary author set, the authors with the same name are treated differently. Specifically, it traverses all the articles of the author with the same name, and compares the similarity of the articles to confirm whether they are in the same field. Similarity comparison can be performed using titles, keywords, and abstracts, such as ICTCLAS, jieba word segmentation, and TF-IDF algorithms. If the topic similarity exceeds the first preset threshold (for example, 60%), it can be determined that the two articles are articles in the same field, and it is preliminarily verified that the author with the same name is the same person. If the topic similarity is found to be lower than the first preset threshold, it is considered that the author with the same name may not be the same person. After the above steps are completed, further processing is required.

Identify the author's institution. The same name is processed according to the corresponding author's institution in the article. If the initial verification is the same person, the time of the author's institution will be identified according to the year of publication of the article. If the initial verification is not the same person, it is necessary to further identify the institution and determine the proportion of institutions with the same name within the same time period (for example, 5 years). One is that two people with the same name in different majors appear in the same institution, and the other is that the person is the same person, but the research direction has changed. Therefore, it is necessary to further determine its collaborators.

If the proportion of the same collaborators in the same time period and in the same institution exceeds the third preset threshold (for example, 30%), the person is considered to be the same person, but the research direction has changed; if the proportion is lower than the third preset threshold , it means that the two are different authors of the same institution. After the above processing, according to the aforementioned rules, authors with the same name from different document data can be distinguished, so as to classify authors and identify institutions.

The embodiment of the present invention also discloses an information push device 30 based on document data. The information push device 30 based on document data may include:

A document data crawling module 301, configured to crawl document data published by multiple users, the document data including document subject and citation document data;

The same name identification module 302 is configured to identify the author with the same name at least according to the subject similarity of the document data if the authors of different document data have the same name;

A document data determination module 303, configured to determine the document data published by the at least some users and the cited document data according to the crawled multiple document data, the published document data includes the number of published documents, and the cited document data including the number of citations;

An evaluation result calculation module 304, configured to calculate an evaluation result of the at least part of the users at least according to the literature data published by the at least part of the users and the cited literature data;

The push module 305 is configured to push information according to the evaluation results of each user, where the information includes the user and/or the user's document data.

In this embodiment of the present invention, the evaluation result of the at least some users is calculated by using the document data published by at least some users and the cited document data, where the published document data includes the number of published documents, and the cited document data includes The number of cited documents and used for information push; compared with the H index in the prior art, the calculated user evaluation results can reflect the user's published document volume and citation frequency, so that the evaluation results can reflect the user's published document data and highly cited data. . In addition, by distinguishing the authors of the same name in the literature, the accuracy of the literature data can be improved, the real situation of the user can be more truly reflected, and the accuracy of information push (such as talent recommendation) can be improved.

For more details on the working principle and working mode of the information push device 30 based on document data, reference may be made to the relevant descriptions in FIG. 1 to FIG. 2 , which will not be repeated here.

An embodiment of the present invention further discloses a storage medium, which is a computer-readable storage medium, and stores a computer program thereon, and when the computer program runs, the steps of the method shown in FIG. 1 or FIG. 2 can be executed. The storage medium may include ROM, RAM, magnetic or optical disks, and the like. The storage medium may also include a non-volatile memory (non-volatile) or a non-transitory (non-transitory) memory and the like.

An embodiment of the present invention further discloses a terminal, where the terminal may include a memory and a processor, and the memory stores a computer program that can run on the processor. The processor may execute the steps of the method shown in FIG. 1 or FIG. 2 when running the computer program. The terminals include but are not limited to terminal devices such as mobile phones, computers, and tablet computers.

The terminal in this embodiment of the present application may refer to various forms of terminal, access terminal, subscriber unit, subscriber station, mobile station, mobile station (mobile station, built as MS), remote station, remote terminal, mobile device, user terminal, Terminal equipment, wireless communication equipment, user agent or user equipment. The terminal device may also be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), Handheld devices, computing devices or other processing devices connected to wireless modems, in-vehicle devices, wearable devices with wireless communication capabilities, terminal devices in future 5G networks or future evolved public land mobile communication networks (Public Land Mobile Network, referred to as PLMN), which is not limited in this embodiment of the present application.

It should be understood that the term "and/or" in this document is only an association relationship to describe associated objects, indicating that there can be three kinds of relationships, for example, A and/or B, which can mean that A exists alone, and A and B exist at the same time , there are three cases of B alone. In addition, the character "/" in this text indicates that the related objects before and after are an "or" relationship.

The "plurality" in the embodiments of the present application refers to two or more.

The descriptions of the first, second, etc. appearing in the embodiments of the present application are only used for illustration and distinguishing the description objects, and have no order. any limitations of the examples.

The "connection" in the embodiments of the present application refers to various connection modes such as direct connection or indirect connection, so as to realize communication between devices, which is not limited in the embodiments of the present application.

It should be understood that in the embodiment of the present application, the processor may be a central processing unit (central processing unit, CPU for short), and the processor may also be other general-purpose processors, digital signal processors (digital signal processors, DSP for short), special-purpose processors An integrated circuit (application specific integrated circuit, ASIC for short), an off-the-shelf programmable gate array (field programmable gate array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It should also be understood that the memory in the embodiments of the present application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be a read-only memory (ROM for short), a programmable read-only memory (PROM for short), an erasable PROM for short (EPROM) , Electrically Erasable Programmable Read-Only Memory (electrically EPROM, EEPROM for short) or flash memory. The volatile memory may be random access memory (RAM for short), which is used as an external cache memory. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic Random access memory (synchronous DRAM, referred to as SDRAM), double data rate synchronous dynamic random access memory (double data rate SDRAM, referred to as DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, referred to as ESDRAM), synchronous connection Dynamic random access memory (synchlink DRAM, referred to as SLDRAM) and direct memory bus random access memory (direct rambus RAM, referred to as DR RAM).

The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server or data center Transmission by wire or wireless to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, a data center, or the like that contains one or more sets of available media. The usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media. The semiconductor medium may be a solid state drive.

In the several embodiments provided in this application, it should be understood that the disclosed method, apparatus and system may be implemented in other manners. For example, the device embodiments described above are only illustrative; for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may be physically included individually, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional units.

The above-mentioned integrated units implemented in the form of software functional units can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM for short), Random Access Memory (RAM for short), magnetic disk or CD, etc. that can store program codes medium.

Although the present invention is disclosed above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be based on the scope defined by the claims.

Claims (10)

1. An information pushing method based on literature data is characterized by comprising the following steps:

crawling literature data published by a plurality of users, wherein the literature data comprises literature subjects and cited literature data;

if the authors of different literature data are homonymous, identifying the homonymous authors at least according to the topic similarity of the literature data;

extracting published document data and cited document data of at least some users in the plurality of users according to the crawled document data, wherein the published document data comprises the published document number, and the cited document data comprises the cited document number;

calculating the evaluation result of the at least part of users according to the literature data published by the at least part of users and the cited literature data;

and pushing information according to the evaluation result of each user, wherein the pushed information comprises the user and/or the literature data of the user.

2. The information pushing method based on literature data according to claim 1, wherein the calculating the evaluation result of the at least some users according to at least the literature data published by the at least some users and the cited literature data comprises:

calculating the total amount of cited documents by using the cited document data;

determining a zero cited document quantity in the published document data and calculating a weighted sum of the zero cited document quantity and a remaining document quantity;

and calculating the evaluation result of at least part of users according to the total amount of the cited documents and the weighted sum.

3. The information pushing method based on literature data according to claim 1, wherein the calculating the evaluation result of the at least some users according to at least the literature data published by the at least some users and the cited literature data comprises:

calculating a total number of cited documents using the cited document data;

determining a zero cited document quantity in the published document data and calculating a weighted sum of the zero cited document quantity and a remaining document quantity;

calculating an H index of the at least some users;

and combining the H index with the total amount of the cited documents and the weighted sum to obtain the evaluation result of at least part of users.

4. The method according to claim 1, wherein the determining the published literature data and the cited literature data of at least some users according to the crawled literature data comprises:

establishing a document sending database according to a plurality of crawled documents, wherein the document sending database comprises document data published by each user;

establishing a citation database according to a plurality of crawled documents, wherein the citation database comprises document data quoted by document data published by each user;

and determining the quantity of the document data published by at least part of users and the quantity of cited documents according to the document publishing database and the citation database.

5. The method according to claim 1, wherein crawling published literature data of multiple users comprises:

and standardizing the authors and the issuing institutions in the literature data according to a preset format.

6. The information pushing method based on literature data according to claim 1, wherein said identifying authors of the same name at least according to topic similarity of literature data comprises:

calculating topic similarity between the literature data;

if the topic similarity is smaller than a first preset threshold value, determining the proportion of the appearance of the same-name organization in the document published by the same-name author in the same time period;

if the ratio is larger than a second preset threshold value, determining the ratio of the collaborators in the same time period and the same mechanism of the document published by the same author;

and if the ratio is larger than a third preset threshold value, determining that the same author is the same author, otherwise, determining that the same author is different authors.

7. The information pushing method based on literature data according to any one of claims 1 to 6, wherein the literature data comprises title, year, source, organization, keyword and abstract; the types of documents include papers, patents, books, and meeting reports.

8. An information pushing apparatus based on document data, comprising:

the document data crawling module is used for crawling document data published by a plurality of users, and the document data comprises document subjects and cited document data;

the homonymy identification module is used for identifying homonymy authors according to the topic similarity of the literature data at least if the authors of different literature data have the same name;

the literature data determining module is used for determining published literature data and cited literature data of at least part of users according to a plurality of crawled literature data, wherein the published literature data comprises published literature quantity, and the cited literature data comprises cited literature quantity;

the evaluation result calculation module is used for calculating the evaluation results of the at least part of users at least according to the literature data published by the at least part of users and the cited literature data;

and the pushing module is used for pushing information according to the evaluation result of each user, and the pushed information comprises the user and/or the literature data of the user.

9. A storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to perform the steps of the document data based information pushing method according to any one of claims 1 to 7.

10. A terminal comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and the processor executes the computer program to perform the steps of the information pushing method based on literature data according to any one of claims 1 to 7.

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