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CN109002507A - A kind of social user's recommended method that big data platform is combined with brand vehicle - Google Patents

A kind of social user's recommended method that big data platform is combined with brand vehicle Download PDF

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Publication number
CN109002507A
CN109002507A CN201810703587.0A CN201810703587A CN109002507A CN 109002507 A CN109002507 A CN 109002507A CN 201810703587 A CN201810703587 A CN 201810703587A CN 109002507 A CN109002507 A CN 109002507A
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big data
data platform
social
user
social user
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向湘杰
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Dongguan Huarui Electronic Technology Co Ltd
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Dongguan Huarui Electronic Technology Co Ltd
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Priority to CN201810703587.0A priority Critical patent/CN109002507A/en
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    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00Handling natural language data
    • G06F40/20Natural language analysis
    • G06F40/279Recognition of textual entities
    • G06F40/289Phrasal analysis, e.g. finite state techniques or chunking
    • G06Q10/40

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  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

一种大数据平台与品牌车型相结合的社交用户推荐方法,特点是:在检测到第一社交用户完成对某一品牌车型的关注后,检测第一社交用户上报的第一社交用户的用户属性及对该品牌车型的第一评论;从已关注该品牌车型的社交用户集合中查询出不属于第一社交用户的好友的第二社交用户,第二社交用户的用户属性与第一社交用户的用户属性相匹配;对比第一评论与第二社交用户上报的对该品牌车型的第二评论,若两者近似,向第一社交用户发送至少包括第二社交用户对应的社交账号、第二社交用户的用户属性及第二评论的推荐信息,使第一社交用户有效的找到与自己关注的品牌车型存在近似看法的其他社交用户进行交流,便于深入的了解自己关注的品牌车型的口碑。

A social user recommendation method combining a big data platform and a brand model, characterized by: after detecting that the first social user has paid attention to a certain brand model, detecting the user attributes of the first social user reported by the first social user And the first comment on the brand model; from the set of social users who have paid attention to the brand model, query the second social user who is not a friend of the first social user, and the user attributes of the second social user are the same as those of the first social user. User attributes match; compare the first comment with the second comment on the brand model reported by the second social user, and if the two are similar, send at least the social account corresponding to the second social user, the second social comment to the first social user. The user attributes of the user and the recommendation information of the second comment enable the first social user to effectively find other social users who have similar views on the brand models they care about and communicate with them, so as to gain an in-depth understanding of the word-of-mouth of the brand models they care about.

Description

A kind of social user's recommended method that big data platform is combined with brand vehicle
Technical field
The present invention relates to the social use that social technical field more particularly to a kind of big data platform are combined with brand vehicle Family recommended method.
Background technique
Currently, social user can log in car website (such as vapour that internet provides by social account (such as QQ account) Vehicle shopping guide website), and social user, after logging in the car website that internet provides by social account, social user can To pay close attention on car website oneself interested a certain brand vehicle (such as BMW X6).
In practice, it has been found that social user occasionally wants to, there are other societies of approximate view with the brand vehicle of oneself concern User is handed over to exchange, in order to the public praise of the brand vehicle for understanding oneself concern that can be more deep.However, in existing skill In art, social user is difficult to effectively find other social users' progress with the brand vehicle of oneself concern there are approximate view Exchange.
Summary of the invention
Social user's recommended method that the embodiment of the invention discloses a kind of big data platforms to combine with brand vehicle, energy Enough effectively find exchanges with other social activity users that the brand vehicle of oneself concern has approximate view, in order to can be with The public praise of the more deep brand vehicle for understanding oneself concern.
Wherein, social user's recommended method that a kind of big data platform is combined with brand vehicle, comprising:
The big data platform prompts institute after detecting that the first social user completes to the concern of a certain brand vehicle The user property of the first social user described in the first social reporting of user and the first social user are stated to the brand vehicle First comment of type;
The big data platform detect the user property of the described first social user of the described first social reporting of user with And first comment of the first social activity user to the brand vehicle;
The big data platform is according to the user property of the described first social user, from the society for having focused on the brand vehicle Hand over the second social user that the good friend for being not belonging to the described first social user is inquired in user's set;Described second social user The user property of the social user of described second reported and the user property of the described first social user match;
The big data platform obtains the described second social user of the described second social reporting of user to the brand vehicle Second comment of type;
The big data platform compares first comment and second comment;
If comparison shows that first comment is approximate with second comment, the big data platform is social to described first User sends recommendation information, and the recommendation information includes at least the second social activity user corresponding social account, described second The user property of social user and second comment.
As an alternative embodiment, being commented comparing first comment with described second in the embodiment of the present invention By after approximation and before the big data platform sends recommendation information to the described first social user, the method is also wrapped It includes:
The big data platform sends inquiry message to the described second social user, and the inquiry message is described for inquiring Whether the second social user allows to be recommended to other social users;
If the second social user feedback allows to be recommended to other social users, described in the big data platform executes The step of sending recommendation information to the described first social user.
As an alternative embodiment, in the embodiment of the present invention, the big data platform by first comment with Second comment compares, comprising:
The big data platform is disassembled first comment to obtain each candidate sentences;
The big data platform determines the importance scores of each candidate sentences;
The big data platform extracts the target sentence that the importance scores are greater than preset value from each candidate sentences Key message of the son as first comment;
The big data platform carries out the key message of key message that described first comments on and second comment pair Than.
As an alternative embodiment, in the embodiment of the present invention, the big data platform by described first comment on into Row dismantling obtains each candidate sentences, comprising:
The big data platform obtains preset text dismantling rule, the preset text dismantling rule include branch, Comma, fullstop will be disassembled, and pause mark, colon, quotation marks are without dismantling;
The big data platform disassembles rule according to the preset text, and the sight spot first comment is disassembled, To obtain each candidate sentences.
As an alternative embodiment, the big data platform determines each candidate sentence in the embodiment of the present invention The importance scores of son, comprising:
It is described big if the candidate sentences are Chinese sentence for each candidate sentences in each candidate sentences The Chinese sentence is split as several phrases in the way of semantic analysis by data platform;
The big data platform carries out full text traversal to each phrase that fractionation obtains and calculates, and obtains the appearance of each phrase Number;
The big data platform carries out all phrases that fractionation obtains according to the sequence of the frequency of occurrence from high to low Sequence, and each phrase assigns corresponding weight according to the frequency of occurrence, and the frequency of occurrence is higher, and the weight is got over It is high;
The big data platform calculates the importance scores of each Chinese sentence, and the importance scores are the Chinese The weights sum for several phrases that sentence is split.
As an alternative embodiment, in the embodiment of the present invention, social activity user's recommended method further include:
If the candidate sentences are webpage link address, the big data platform with opening the web page interlinkage from the background The corresponding target webpage in location;
The big data platform determines the target network according to the link in the target webpage, being directed toward the target webpage The importance scores of page, the importance scores of the target webpage are the importance scores of the candidate sentences.
As an alternative embodiment, the big data platform determines the target webpage in the embodiment of the present invention Importance scores process are as follows:
Wherein, S (Vi) be target webpage importance scores, d is damped coefficient, and In (Vi) is to exist to be directed toward target webpage Link collections of web pages, out (Vj) it is the collections of web pages that the existing link of link in webpage j is directed toward, out (Vj) take absolutely Value is the number to indicate element in the collections of web pages, S (Vj) be webpage j importance scores.
As an alternative embodiment, in the embodiment of the present invention, what the big data platform was commented on described first Key message and the key message of second comment compare, comprising:
The big data platform determines the first sentence in the key message of first comment, and determines described second The second sentence in the key message of comment;Wherein, the phrase to overlap each other between first sentence and second sentence Quantity it is most;
The big data platform calculates the cosine similarity of first sentence Yu second sentence;
If the cosine similarity is higher than designated value, the big data platform determines that first comment is commented with described second By approximation.
As an alternative embodiment, in the embodiment of the present invention, the calculating process of the cosine similarity are as follows:
First sentence is split as several phrases by the big data platform, to obtain one group of phrase;
Second sentence is split as several phrases by the big data platform, to obtain another group of phrase;
The big data platform is compared the phrase in two groups of phrases one by one, and if it exists, 1 is then recorded as, if not depositing It is being recorded as 0, then to obtain First ray and the second sequence;
The big data platform calculates the cosine similarity between the First ray and second sequence, and as institute State the cosine similarity between the first sentence and second sentence;
Wherein, the big data platform calculates the cosine similarity between the First ray and second sequence are as follows:
Wherein, ab is integrally added after indicating the element in a sequence and corresponding element multiplication in b sequence, and denominator indicates a sequence The quadratic sum of all elements is opened and opens radical sign multiplied by the quadratic sum of all elements in b sequence after radical sign in column;Wherein, a sequence indicates The First ray, b sequence indicate second sequence.
As an alternative embodiment, the big data platform detection described first is social in the embodiment of the present invention The user property of the social user of described the first of reporting of user and the first social user are to the first of the brand vehicle After comment and the big data platform is according to the user property of the described first social user, from having focused on the brand vehicle Before the second social user for inquiring the good friend for being not belonging to the described first social user in the social user set of type, the side Method further include:
The social reporting of user additional searching information of the big data platform prompt described first;
The big data platform detects the additional searching information of the described first social reporting of user;The additional searching Information includes at least search time section and region of search;
The big data platform obtains the described second social user of the described second social reporting of user to the brand vehicle After second comment of type, the method also includes:
The big data platform inquire the second comment described in the described second social reporting of user on call time and whether be located at In the described search period;
It is described if calling time within the described search period in the second comment described in the described second social reporting of user Whether the current location that big data platform inquires the described second social user is located in described search region;
If the current location of the described second social user is located in described search region, described in the big data platform execution By it is described first comment with it is described second comment compare the step of.
In the embodiment of the present invention, big data platform is detecting concern of the first social user's completion to a certain brand vehicle Later, it can detecte the first social user according to the user property for prompting the report first social user and the first social user The first comment to the brand vehicle;And big data platform can be from the social user set for having focused on the brand vehicle The second social user for the good friend for being not belonging to the first social user is inquired, the second social user's of the second social reporting of user User property and the user property of the first social user match;And big data platform can be by the first comment and the second society The second social user of reporting of user is handed over to compare the second comment of the brand vehicle, if comparison shows the first comment and the Two comments are approximate, and big data platform is sent to the first social user includes at least the corresponding social account of the second social user, the The user property of two social users and the recommendation information of the second comment, allow the first social user effectively find with from Other social users that the brand vehicle of oneself concern has approximate view exchange, in order to can be more deep be known from The public praise of the brand vehicle of oneself concern.
Detailed description of the invention
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to needed in the embodiment Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for ability For the those of ordinary skill of domain, without creative efforts, it can also be obtained according to these attached drawings other attached Figure.
Fig. 1 is a kind of social user recommendation side that big data platform is combined with brand vehicle disclosed by the embodiments of the present invention The flow diagram of method;
Fig. 2 is the method flow schematic diagram that the first comment disclosed by the embodiments of the present invention is compared with the second comment;
Fig. 3 is that the social user that another big data platform disclosed by the embodiments of the present invention is combined with brand vehicle recommends The flow diagram of method.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that the described embodiment is only a part of the embodiment of the present invention, instead of all the embodiments.Based on this Embodiment in invention, every other reality obtained by those of ordinary skill in the art without making creative efforts Example is applied, shall fall within the protection scope of the present invention.
It should be noted that the term " includes " of the embodiment of the present invention and " having " and their any deformation, it is intended that Be to cover it is non-exclusive include, for example, containing the process, method, system, product or equipment of a series of steps or units not Those of be necessarily limited to be clearly listed step or unit, but may include be not clearly listed or for these processes, side The intrinsic other step or units of method, product or equipment.
Social user's recommended method that the embodiment of the invention discloses a kind of big data platforms to combine with brand vehicle, energy Enough effectively find exchanges with other social activity users that the brand vehicle of oneself concern has approximate view, in order to can be with The public praise of the more deep brand vehicle for understanding oneself concern.Attached drawing is combined below to be described in detail.
Referring to Fig. 1, Fig. 1 is a kind of social activity that big data platform is combined with brand vehicle disclosed by the embodiments of the present invention The flow diagram of user's recommended method.As shown in Figure 1, the social user that the big data platform is combined with brand vehicle recommends Method may comprise steps of:
101, big data platform is after detecting that the first social user completes to the concern of a certain brand vehicle, prompts the First comment of the user property and the first social user of the one social user of social reporting of user first to the brand vehicle.
In the embodiment of the present invention, the first social user can log in internet by its social account (such as QQ account) and provide Car website (such as automobile shopping guide website), and the first social user is in the vapour for logging in internet offer by its social account After vehicle website, the first social user can on car website to oneself interested a certain brand vehicle (such as BMW X6) into Row concern;Correspondingly, big data platform is detecting that the first social user completes on car website to a certain brand vehicle The user property that can prompt the first social user of social reporting of user first after concern and the first social user are to the product First comment of board vehicle.For example, big data platform, which can control the car website, pops up the first interactive interface, first interaction Interface includes user property for prompting the first social user of social reporting of user first and the first social user to the product The prompt information of first comment of board vehicle and for the user property for the first social user of social reporting of user first with And first social user to the reporting port of the first comment of the brand vehicle.
Wherein, brand vehicle is the combination of brand and vehicle, such as when brand is BMW, vehicle is X6, brand vehicle are as follows: BMW X6.
102, the user property of the first social user of the social reporting of user of big data platform detection first and the first society Hand over first comment of the user to the brand vehicle.
In the embodiment of the present invention, what the first social user can include according to first interactive interface is used to prompt the first society Hand over the prompt of the user property and the first social user of the social user of reporting of user first to the first comment of the brand vehicle The prompt of information, the reporting port for including by first interactive interface report the user of the first social user to big data platform The first comment of attribute and the first social user to the brand vehicle.
In the embodiment of the present invention, user property may include age of user and gender.
103, big data platform is according to the user property of the first social user, from the social user for having focused on the brand vehicle The second social user for the good friend for being not belonging to the first social user is inquired in set;Wherein, the of the second social reporting of user The user property of two social users and the user property of the first social user match.
As an alternative embodiment, in the embodiment of the present invention, after big data platform executes step 102, and Before executing step 103, following steps can also be performed:
Big data platform judges whether the first social user has opened that there are approximate views with the brand vehicle of oneself concern Social user recommend business;
If the first social user has opened, there are the social users of approximate view to recommend industry with the brand vehicle of oneself concern Business, big data platform can execute step 103;Conversely, being deposited if the first social user does not open with the brand vehicle of oneself concern Recommend business in the social user of approximate view, big data platform can terminate this process.
Wherein, implement above embodiment, can prevent to not yet open with oneself pay close attention to brand vehicle there are approximate When the social user of view recommends the first social user of business to recommend other social users, the first social user is caused to recommend Harassing and wrecking.
104, the second social user that big data platform obtains the second social reporting of user comments the second of the brand vehicle By.
105, big data platform compares the first comment and the second comment.
If 106, comparison shows that the first comment is approximate with the second comment, big data platform is sent to the first social user recommends Information, recommendation information include at least the corresponding social account of the second social user, the user property of the second social user and the Two comments.
Wherein, the first social user can comment on according to the user property of the second social user and second, decide whether It needs to be made friends according to the corresponding social account of the second social activity user with the second social user.
As an alternative embodiment, big data platform comments on the first comment with second in above-mentioned step 105 It compares, may comprise steps of:
201, big data platform is disassembled the first comment to obtain each candidate sentences;
202, big data platform determines the importance scores of each candidate sentences;
203, big data platform extracts target sentences of the importance scores greater than preset value as the from each candidate sentences The key message of one comment;
204, big data platform compares first key message commented on and the key message of second comment.
As an alternative embodiment, first comment is disassembled to obtain by big data platform in above-mentioned steps 201 Each candidate sentences may include:
Big data platform obtains preset text dismantling rule, which includes branch, comma, sentence It number to be disassembled, and pause mark, colon, quotation marks are without dismantling;
And big data platform disassembles rule according to the preset text, the first comment is disassembled, to obtain each time Select sentence.
As an alternative embodiment, big data platform determines the importance of each candidate sentences in above-mentioned steps 202 Score may include:
For each candidate sentences in each candidate sentences, if the candidate sentences are Chinese sentence, big data platform is pressed The Chinese sentence is split as several phrases according to the mode of semantic analysis;
And big data platform carries out full text traversal to each phrase that fractionation obtains and calculates, and obtains going out for each phrase Occurrence number;
And big data platform is arranged according to all phrases that the sequence of frequency of occurrence from high to low obtains fractionation Sequence, and each phrase assigns corresponding weight according to frequency of occurrence, and frequency of occurrence is higher, and the weight is higher;
And big data platform calculates the importance scores of each Chinese sentence, importance scores are the Chinese sentence Split the weights sum of several obtained phrases.
For example, the first comment are as follows:
The oil consumption of XX brand vehicle is low, space is big, power is sufficient, comfort is good;The cost performance of XX brand vehicle is high;XX brand The appearance of vehicle is beautiful, interior trim is luxurious.
Correspondingly, big data platform disassembles rule according to the preset text, the first comment is disassembled, what is obtained is each Candidate sentences include:
Candidate sentences include:
A, the oil consumption of XX brand vehicle is low, space is big, power is sufficient, comfort is good;
B, the cost performance of XX brand vehicle is high;
C, the appearance of XX brand vehicle is beautiful, interior trim is luxurious.
Correspondingly, the phrase that big data platform is disassembled includes:
XX brand vehicle;Occur 3 times, weight 1;
Oil consumption is low;Occur 1 time, weight 1
Space is big;Occur 1 time, weight 1
Power foot;Occur 1 time, weight 1
Comfort is good;Occur 1 time, weight 1
Cost performance is high;Occur 1 time, weight 1
Appearance: occur 1 time, weight 1
It is beautiful: to occur 1 time, weight 1
Interior trim;Occur 1 time, weight 1
It is luxurious;Occur 1 time, weight 1.
Correspondingly, the importance scores of candidate sentences above are respectively as follows: No. A 7 points, No. B 4 points, No. C 7 points.
Further, it is assumed that preset value is 6 points, then target sentences are No. A and No. C;
Correspondingly, the key message of the key message of first comment are as follows: the oil consumption of XX brand vehicle is low, space is big, dynamic Power foot, comfort are good;The appearance of XX brand vehicle is beautiful, interior trim is luxurious.
As one kind optionally implement it is above-mentioned, if the not Chinese sentence of the candidate sentences, webpage link address, that Big data platform can open from the background the corresponding target webpage of the webpage link address;And big data platform can root The importance scores of target webpage, the importance point of the target webpage are determined according to the link in target webpage, being directed toward target webpage Number is the importance scores of the candidate sentences.
Wherein it is determined that the process of the importance scores of target webpage are as follows:
Wherein, S (Vi) be target webpage importance scores, d is damped coefficient, and In (Vi) is to exist to be directed toward target webpage Link collections of web pages, out (Vj) it is the collections of web pages that the existing link of link in webpage j is directed toward, out (Vj) take absolutely Value is the number to indicate element in collections of web pages, S (Vj) be webpage j importance scores.
As an alternative embodiment, the key message of first comment is actually the set of sentence, to pass The comparison of key information is actually to compare to sentence.Therefore, in above-mentioned steps 204, big data platform first comments this The key message of opinion and the key message of second comment compare, and may include:
Big data platform determines the first sentence in the key message of first comment, and determines the key of the second comment The second sentence in information;Wherein, the quantity of the phrase to overlap each other between the first sentence and the second sentence is most;
And big data platform calculates the cosine similarity of the first sentence and the second sentence;
If cosine similarity is higher than designated value, big data platform determines that the first comment is approximate with the second comment.
In the embodiment of the present invention, the calculating process of above-mentioned cosine similarity are as follows:
First sentence is split as several phrases by big data platform, to obtain one group of phrase;
And the second sentence is split as several phrases by big data platform, to obtain another group of phrase;
And big data platform is compared the phrase in two groups of phrases one by one, and if it exists, 1 is then recorded as, if not depositing It is being recorded as 0, then to obtain First ray and the second sequence;
And big data platform calculates the cosine similarity between First ray and the second sequence, and as the first sentence And the second cosine similarity between sentence.
As an example it is assumed that:
First sentence are as follows: the oil consumption of XX brand vehicle is low, space is big, power is sufficient, comfort is good;
Second sentence are as follows: the comfort of XX brand vehicle is good, space is big, power is sufficient;
So, the comparison for two groups of phrases that the first sentence and the second sentence are split into is as shown in table 1, it may be assumed that
Table 1
XX brand vehicle Oil consumption is low Space is big Power foot Comfort is good
First sentence 1 1 1 1 1
Second sentence 1 0 1 1 1
As shown in table 1, First ray a is (1,1,1,1), and the second sequence b is (1,1,0,1).
Wherein, big data platform calculates the cosine similarity between First ray and the second sequence are as follows:
Wherein, ab is integrally added after indicating the element in a sequence and corresponding element multiplication in b sequence, and denominator indicates a sequence The quadratic sum of all elements is opened and opens radical sign multiplied by the quadratic sum of all elements in b sequence after radical sign in column;Wherein, a sequence indicates First ray, b sequence indicate the second sequence.
For example, the result that the first above-mentioned sentence and the second sentence calculate be cos (a, b)=0.89 (i.e. First ray and Cosine similarity between second sequence), be higher than designated value when 0.89, big data platform can determine first comment with this Two comments are approximate.
In the embodiment of the present invention, the above-mentioned implementation to step 105 is described in detail, and implements above-mentioned embodiment party Formula can promote the accuracy that the first comment is compared with the second comment, so as to accurately judge to report first to comment There are approximate views between the social user of the first of opinion and the second social user for reporting the second comment.
Wherein, implement method described in Fig. 1, find the first social user effectively, accurately and closed with oneself Other social users that the brand vehicle of note has approximate view exchange, in order to understanding oneself pass that can be more deep The public praise of the brand vehicle of note.
Referring to Fig. 3, Fig. 3 is the society that another big data platform disclosed by the embodiments of the present invention is combined with brand vehicle Hand over the flow diagram of user's recommended method.As shown in figure 3, the social user that the big data platform is combined with brand vehicle pushes away The method of recommending may comprise steps of:
301, big data platform is after detecting that the first social user completes to the concern of a certain brand vehicle, prompts the First comment of the user property and the first social user of the one social user of social reporting of user first to the brand vehicle.
302, the user property of the first social user of the social reporting of user of big data platform detection first and the first society Hand over first comment of the user to the brand vehicle.
As an alternative embodiment, in the embodiment of the present invention, after big data platform executes step 302, and Before executing step 303, following steps can also be performed:
Big data platform judges whether the first social user has opened that there are approximate views with the brand vehicle of oneself concern Social user recommend business;
If the first social user has opened, there are the social users of approximate view to recommend industry with the brand vehicle of oneself concern Business, big data platform can execute step 303;Conversely, being deposited if the first social user does not open with the brand vehicle of oneself concern Recommend business in the social user of approximate view, big data platform can terminate this process.
Wherein, implement above embodiment, can prevent to not yet open with oneself pay close attention to brand vehicle there are approximate When the social user of view recommends the first social user of business to recommend other social users, the first social user is caused to recommend Harassing and wrecking.
303, the social reporting of user additional searching information of big data platform prompt first.
In the embodiment of the present invention, big data platform can control the car website and pop up the second interactive interface, second friendship Mutual interface includes prompt information for prompt the first social reporting of user additional searching information and for social for first Family reports the reporting port of additional searching information.
304, the additional searching information of the social reporting of user of big data platform detection first;Additional searching information includes at least Search time section and region of search.
In the embodiment of the present invention, what the first social user can include according to second interactive interface is used to prompt the first society The prompt for handing over the prompt information of reporting of user additional searching information, the reporting port for including by second interactive interface are counted to big Additional searching information is reported according to platform, wherein additional searching information includes at least search time section and region of search.
305, big data platform is according to the user property of the first social user, from the social user for having focused on the brand vehicle The second social user for the good friend for being not belonging to the first social user is inquired in set;Wherein, the of the second social reporting of user The user property of two social users and the user property of the first social user match.
306, the second social user that big data platform obtains the second social reporting of user comments the second of the brand vehicle By.
307, the social reporting of user second of big data platform inquiry second comment on call time whether be located at search time In section;If calling time in search time section in the second social comment of reporting of user second, step 308 is executed;If second It calls time and is not located in search time section in the social comment of reporting of user second, terminate this process.
308, whether the current location of the social user of big data platform inquiry second is located in region of search;If second is social The current location of user is located in region of search, executes step 309- step 311;If the current location of the second social user not position In in region of search, terminate this process.
In the embodiment of the present invention, the search time section that the first social user reports to big data platform is by the first social user Self-setting, the such first social reporting of user can be relatively close apart from current time to the search time section of big data platform, from And other social users and approximate view existing for the brand vehicle of first social user's concern can be prevented no longer approximate, thus The first social user can be improved and find the reliable of other social activity users that there is approximate view with the brand vehicle of oneself concern Property.
In the embodiment of the present invention, region of search that the first social user reports to big data platform by the first social user from Row setting, such first social reporting of user may include the present bit of the first social user to the region of search of big data platform It sets, so as to be convenient for and other social users and first of approximate view existing for the brand vehicle of first social user's concern Aspectant exchange is carried out between social user, in region of search in order to which the first social user can more deep, face Opposite understands oneself concern to other social users of approximate view existing for the brand vehicle paid close attention to the first social user Brand vehicle public praise.
309, big data platform compares the first comment and the second comment.
Wherein, the specific implementation of above-mentioned step 309 is described in detail in embodiment in front, the present invention Embodiment does not repeat herein.
If 310, comparison shows that the first comment is approximate with the second comment, big data platform sends inquiry to the second social user Information, inquiry message is for inquiring whether the second social user allows to be recommended to other social users.
If 311, the second social user feedback allows to be recommended to other social users, big data platform is used to the first social activity Family sends recommendation information, and recommendation information includes at least the user of the corresponding social account of the second social activity user, the second social user Attribute and the second comment.
Wherein, implementation steps 310- step 311, can be in the case where the second social user knows, just to the first social activity User, which sends, includes at least the corresponding social account of the second social activity user, the user property of the second social user and the second comment Recommendation information, improve the informed degree of the second social user.
As an alternative embodiment, above-mentioned inquiry message further includes the first social use in the embodiment of the present invention First social user of the corresponding social account in family, the user property of the first social user and the first social reporting of user is to this First comment of brand vehicle.Correspondingly, the second social user can be according to the user property and first of the first social user Comment decides whether to allow to be recommended to the first social user.
Wherein, method described in implementing Fig. 3 finds the first social user effectively, accurately and closes with oneself Other social users that the brand vehicle of note has approximate view exchange, in order to understanding oneself pass that can be more deep The public praise of the brand vehicle of note.
As an alternative embodiment, the first social user is receiving big data platform hair in the embodiment of the present invention That send includes at least the corresponding social account of the second social activity user, the second user property of social user and pushing away for the second comment After recommending information, the corresponding social account of the second social activity user can be added to the social account of good friend by the first social user, And after the good friend that the second social user becomes the first social user, the first social user can with the second social user into Row exchange, in order to the public praise of the brand vehicle for understanding oneself concern that can be more deep.In addition, the first social user may be used also To score the second of the brand vehicle the second social user, and the first social activity user can will be to the second society It hands over user to carry out scoring acquisition score value to the second comment of the brand vehicle and is reported to big data platform;Correspondingly, big data Platform can add up the score value and current general comment score value of the second social user on car website, to generate second Newest general comment score value of the social user on car website;Correspondingly, big data platform can decide whether there are it is described most New general comment score value is corresponding and the still none obtained user right of the second social user is (for example, the purchase vehicle of the brand vehicle of concern Preferential permission), if there is have newest general comment score value corresponding and the second social user still none obtained user right (for example, The preferential permission of the brand vehicle of concern), big data platform can newest general comment score value is corresponding and the second social user Still none obtained user right (for example, preferential permission of purchase vehicle of the brand vehicle of concern) distributes to the second social user.Its In, implement this embodiment, the second social user can be encouraged more to report the brand vehicle to concern to big data platform The comment of type, to not only contribute to other, social user's (the such as first social use) is effective, accurately finds and pays close attention to oneself Brand vehicle the second social user for there is approximate view exchange, also help the second social user and obtain more use Family permission (for example, preferential permission of purchase vehicle of the brand vehicle of concern).
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can It is completed with instructing relevant hardware by program, which can be stored in a computer readable storage medium, storage Medium include read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), programmable read only memory (Programmable Read-only Memory, PROM), erasable programmable is read-only deposits Reservoir (Erasable Programmable Read Only Memory, EPROM), disposable programmable read-only memory (One- Time Programmable Read-Only Memory, OTPROM), the electronics formula of erasing can make carbon copies read-only memory (Electrically-Erasable Programmable Read-Only Memory, EEPROM), CD-ROM (Compact Disc Read-Only Memory, CD-ROM) or other disc memories, magnetic disk storage, magnetic tape storage or can For carrying or any other computer-readable medium of storing data.
Above to a kind of social user that big data platform is combined with brand vehicle recommendation disclosed by the embodiments of the present invention Method is described in detail, and used herein a specific example illustrates the principle and implementation of the invention, with The explanation of upper embodiment is merely used to help understand method and its core concept of the invention;Meanwhile for the general of this field Technical staff, according to the thought of the present invention, there will be changes in the specific implementation manner and application range, in conclusion The contents of this specification are not to be construed as limiting the invention.

Claims (10)

1.一种大数据平台与品牌车型相结合的社交用户推荐方法,其特征在于,所述方法包括:1. A social user recommendation method combining a big data platform and a brand model, characterized in that the method comprises: 所述大数据平台在检测到第一社交用户完成对某一品牌车型的关注之后,提示所述第一社交用户上报所述第一社交用户的用户属性以及所述第一社交用户对所述品牌车型的第一评论;After the big data platform detects that the first social user has paid attention to a certain brand model, it prompts the first social user to report the user attributes of the first social user and the first social user's interest in the brand. The first review of the car model; 所述大数据平台检测所述第一社交用户上报的所述第一社交用户的用户属性以及所述第一社交用户对所述品牌车型的第一评论;The big data platform detects the user attributes of the first social user reported by the first social user and the first comment of the first social user on the brand model; 所述大数据平台根据所述第一社交用户的用户属性,从已关注所述品牌车型的社交用户集合中查询出不属于所述第一社交用户的好友的第二社交用户;所述第二社交用户上报的所述第二社交用户的用户属性与所述第一社交用户的用户属性相匹配;According to the user attributes of the first social user, the big data platform queries a second social user who is not a friend of the first social user from a set of social users who have paid attention to the brand model; The user attribute of the second social user reported by the social user matches the user attribute of the first social user; 所述大数据平台获取所述第二社交用户上报的所述第二社交用户对所述品牌车型的第二评论;The big data platform obtains the second comment on the brand model by the second social user reported by the second social user; 所述大数据平台将所述第一评论与所述第二评论进行对比;The big data platform compares the first comment with the second comment; 若对比表明所述第一评论与所述第二评论近似,所述大数据平台向所述第一社交用户发送推荐信息,所述推荐信息至少包括所述第二社交用户对应的社交账号、所述第二社交用户的用户属性以及所述第二评论。If the comparison shows that the first comment is similar to the second comment, the big data platform sends recommendation information to the first social user, and the recommendation information includes at least the social account corresponding to the second social user, the The user attribute of the second social user and the second comment. 2.根据权利要求1所述的社交用户推荐方法,其特征在于,在对比所述第一评论与所述第二评论近似之后,以及所述大数据平台向所述第一社交用户发送推荐信息之前,所述方法还包括:2. The social user recommendation method according to claim 1, characterized in that, after comparing the first comment and the second comment for similarity, and the big data platform sends recommendation information to the first social user Previously, the method further included: 所述大数据平台向所述第二社交用户发送询问信息,所述询问信息用于询问所述第二社交用户是否允许被推荐给其他社交用户;The big data platform sends inquiry information to the second social user, and the inquiry information is used to inquire whether the second social user is allowed to be recommended to other social users; 若所述第二社交用户反馈允许被推荐给其他社交用户,所述大数据平台执行所述的向所述第一社交用户发送推荐信息的步骤。If the feedback from the second social user is allowed to be recommended to other social users, the big data platform executes the step of sending recommendation information to the first social user. 3.根据权利要求1或2所述的社交用户推荐方法,其特征在于,所述大数据平台将所述第一评论与所述第二评论进行对比,包括:3. The social user recommendation method according to claim 1 or 2, wherein the big data platform compares the first comment with the second comment, including: 所述大数据平台将所述第一评论进行拆解得到各候选句子;The big data platform disassembles the first comment to obtain each candidate sentence; 所述大数据平台确定所述各候选句子的重要性分数;The big data platform determines the importance score of each candidate sentence; 所述大数据平台从所述各候选句子中提取所述重要性分数大于预设值的目标句子作为所述第一评论的关键信息;The big data platform extracts the target sentence whose importance score is greater than a preset value from the candidate sentences as the key information of the first comment; 所述大数据平台将所述第一评论的关键信息与所述第二评论的关键信息进行对比。The big data platform compares the key information of the first comment with the key information of the second comment. 4.根据权利要求3所述的社交用户推荐方法,其特征在于,所述大数据平台将所述第一评论进行拆解得到各候选句子,包括:4. The social user recommendation method according to claim 3, wherein the big data platform disassembles the first comment to obtain each candidate sentence, including: 所述大数据平台获取预设的文本拆解规则,所述预设的文本拆解规则包括分号、逗号、句号要进行拆解,而顿号、冒号、引号不进行拆解;The big data platform obtains preset text disassembly rules, and the preset text disassembly rules include semicolons, commas, and full stops to be disassembled, while commas, colons, and quotation marks are not disassembled; 所述大数据平台按照所述预设的文本拆解规则,将所述景点第一评论进行拆解,以得到各候选句子。The big data platform disassembles the first comment on the attraction according to the preset text disassembly rules to obtain candidate sentences. 5.根据权利要求4所述的社交用户推荐方法,其特征在于,所述大数据平台确定所述各候选句子的重要性分数,包括:5. social user recommendation method according to claim 4, is characterized in that, described big data platform determines the importance score of described each candidate sentence, comprises: 针对所述各候选句子中的每一候选句子,如果所述候选句子为中文句子,所述大数据平台按照语义分析的方式将所述中文句子拆分为若干个词组;For each candidate sentence in each candidate sentence, if the candidate sentence is a Chinese sentence, the big data platform will split the Chinese sentence into several phrases according to the semantic analysis mode; 所述大数据平台对拆分得到的每个词组进行全文遍历计算,得到每个词组的出现次数;The big data platform carries out full-text traversal calculation to each phrase obtained by splitting, and obtains the number of occurrences of each phrase; 所述大数据平台按照所述出现次数由高到低的顺序对拆分得到的所有词组进行排序,并且每个词组按照所述出现次数赋予相应的权值,所述出现次数越高,所述权值越高;The big data platform sorts all the phrases obtained by splitting according to the order of the number of appearances from high to low, and assigns a corresponding weight to each phrase according to the number of appearances, the higher the number of appearances, the higher the number of appearances, the The higher the weight; 所述大数据平台计算每一中文句子的重要性分数,所述重要性分数即为所述中文句子拆分得到的若干个词组的权值之和。The big data platform calculates the importance score of each Chinese sentence, and the importance score is the sum of the weights of several phrases obtained by splitting the Chinese sentence. 6.根据权利要求5所述的社交用户推荐方法,其特征在于,还包括:6. The social user recommendation method according to claim 5, further comprising: 如果所述候选句子为网页链接地址,所述大数据平台在后台开启所述网页链接地址对应的目标网页;If the candidate sentence is a webpage link address, the big data platform opens the target webpage corresponding to the webpage link address in the background; 所述大数据平台根据所述目标网页中,指向所述目标网页的链接确定所述目标网页的重要性分数,所述目标网页的重要性分数即是所述候选句子的重要性分数。The big data platform determines the importance score of the target webpage according to the links pointing to the target webpage in the target webpage, and the importance score of the target webpage is the importance score of the candidate sentence. 7.根据权利要求6所述的社交用户推荐方法,其特征在于,所述大数据平台确定所述目标网页的重要性分数的过程为:7. social user recommendation method according to claim 6, is characterized in that, the process that described big data platform determines the importance score of described target webpage is: 其中,S(Vi)是目标网页的重要性分数,d是阻尼系数,In(Vi)是存在指向目标网页的链接的网页集合,out(Vj)是网页j中的链接存在的链接指向的网页集合,out(Vj)取绝对值是用以表示所述网页集合中元素的个数,S(Vj)是网页j的重要性分数。Among them, S(V i ) is the importance score of the target web page, d is the damping coefficient, In(Vi) is the set of web pages with links pointing to the target web page, and out(V j ) is the link pointing to The set of webpages, the absolute value of out(V j ) is used to represent the number of elements in the set of webpages, and S(V j ) is the importance score of webpage j. 8.根据权利要求3-7任一项所述的社交用户推荐方法,其特征在于,所述大数据平台将所述第一评论的关键信息与所述第二评论的关键信息进行对比,包括:8. The social user recommendation method according to any one of claims 3-7, wherein the big data platform compares the key information of the first comment with the key information of the second comment, including : 所述大数据平台确定所述第一评论的关键信息中的第一句子,以及确定所述第二评论的关键信息中的第二句子;其中,所述第一句子与所述第二句子之间彼此重叠的词组的数量最多;The big data platform determines the first sentence in the key information of the first comment, and determines the second sentence in the key information of the second comment; wherein, the difference between the first sentence and the second sentence The number of phrases overlapping with each other is the largest; 所述大数据平台计算所述第一句子与所述第二句子的余弦相似度;The big data platform calculates the cosine similarity between the first sentence and the second sentence; 若所述余弦相似度高于指定值,所述大数据平台确定所述第一评论与所述第二评论近似。If the cosine similarity is higher than a specified value, the big data platform determines that the first comment is similar to the second comment. 9.根据权利要求8所述的社交用户推荐方法,其特征在于,所述余弦相似度的计算过程为:9. The social user recommendation method according to claim 8, wherein the calculation process of the cosine similarity is: 所述大数据平台将所述第一句子拆分为若干个词组,以获得一组词组;The big data platform splits the first sentence into several phrases to obtain a set of phrases; 所述大数据平台将所述第二句子拆分为若干个词组,以获得另一组词组;The big data platform splits the second sentence into several phrases to obtain another phrase; 所述大数据平台将两组词组中的词组进行逐一对比,若存在,则记录为1,若不存在,则记录为0,以得到第一序列和第二序列;The big data platform compares the phrases in the two groups of phrases one by one, if it exists, it is recorded as 1, if it does not exist, it is recorded as 0, to obtain the first sequence and the second sequence; 所述大数据平台计算所述第一序列和所述第二序列之间的余弦相似度,并作为所述第一句子和所述第二句子之间的余弦相似度;The big data platform calculates the cosine similarity between the first sequence and the second sequence as the cosine similarity between the first sentence and the second sentence; 其中,所述大数据平台计算所述第一序列和所述第二序列之间的余弦相似度为:Wherein, the cosine similarity between the first sequence and the second sequence calculated by the big data platform is: 其中,ab表示a序列中的元素与b序列中相应的元素相乘后整体相加,分母表示a序列中所有元素的平方和开根号后乘以b序列中所有元素的平方和开根号;其中,a序列表示所述第一序列,b序列表示所述第二序列。Among them, ab means that the elements in the a sequence are multiplied by the corresponding elements in the b sequence and added as a whole, and the denominator represents the square sum of all the elements in the a sequence and the root sign is multiplied by the square sum of all the elements in the b sequence ; wherein, sequence a represents the first sequence, and sequence b represents the second sequence. 10.根据权利要求1-9任一项所述的社交用户推荐方法,其特征在于,所述大数据平台检测所述第一社交用户上报的所述第一社交用户的用户属性以及所述第一社交用户对所述品牌车型的第一评论之后,以及所述大数据平台根据所述第一社交用户的用户属性,从已关注所述品牌车型的社交用户集合中查询出不属于所述第一社交用户的好友的第二社交用户之前,所述方法还包括:10. The social user recommendation method according to any one of claims 1-9, wherein the big data platform detects the user attributes of the first social user reported by the first social user and the first social user After a social user makes the first comment on the brand model, and according to the user attributes of the first social user, the big data platform inquires from the set of social users who have paid attention to the brand model that they do not belong to the brand model. Before a second social user of a friend of a social user, the method further includes: 所述大数据平台提示所述第一社交用户上报附加搜索信息;The big data platform prompts the first social user to report additional search information; 所述大数据平台检测所述第一社交用户上报的所述附加搜索信息;所述附加搜索信息至少包括搜索时间段和搜索区域;The big data platform detects the additional search information reported by the first social user; the additional search information includes at least a search time period and a search area; 所述大数据平台获取所述第二社交用户上报的所述第二社交用户对所述品牌车型的第二评论之后,所述方法还包括:After the big data platform obtains the second comment of the second social user on the brand model reported by the second social user, the method further includes: 所述大数据平台查询所述第二社交用户上报所述第二评论的上报时间是否位于所述搜索时间段内;The big data platform inquires whether the reporting time of the second social user reporting the second comment is within the search time period; 若所述第二社交用户上报所述第二评论的上报时间位于所述搜索时间段内,所述大数据平台查询所述第二社交用户的当前位置是否位于所述搜索区域内;If the reporting time of the second comment reported by the second social user is within the search time period, the big data platform queries whether the current location of the second social user is within the search area; 若所述第二社交用户的当前位置位于所述搜索区域内,所述大数据平台执行所述的将所述第一评论与所述第二评论进行对比的步骤。If the current location of the second social user is within the search area, the big data platform executes the step of comparing the first comment with the second comment.
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