WO2016000555A1 - 基于社交网络的内容、新闻推荐方法和系统 - Google Patents

基于社交网络的内容、新闻推荐方法和系统 Download PDF

Info

Publication number
WO2016000555A1
WO2016000555A1 PCT/CN2015/082282 CN2015082282W WO2016000555A1 WO 2016000555 A1 WO2016000555 A1 WO 2016000555A1 CN 2015082282 W CN2015082282 W CN 2015082282W WO 2016000555 A1 WO2016000555 A1 WO 2016000555A1
Authority
WO
WIPO (PCT)
Prior art keywords
user
social network
news
interest
content
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2015/082282
Other languages
English (en)
French (fr)
Inventor
周楠
常富洋
秦吉胜
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Qihoo Technology Co Ltd
Qizhi Software Beijing Co Ltd
Original Assignee
Beijing Qihoo Technology Co Ltd
Qizhi Software Beijing Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from CN201410308039.XA external-priority patent/CN104063476A/zh
Priority claimed from CN201410307116.XA external-priority patent/CN104036038A/zh
Application filed by Beijing Qihoo Technology Co Ltd, Qizhi Software Beijing Co Ltd filed Critical Beijing Qihoo Technology Co Ltd
Priority to US15/323,306 priority Critical patent/US20170154116A1/en
Publication of WO2016000555A1 publication Critical patent/WO2016000555A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9536—Search customisation based on social or collaborative filtering
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/40—Business processes related to social networking or social networking services
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/40—Business processes related to social networking or social networking services
    • G06Q10/42—Determination of affinities or common interests between users

Definitions

  • the present invention relates to the field of information technology, and in particular, to a social network-based content recommendation method and system, and a news recommendation method and system.
  • the current Internet news reading products mainly include the web (web) side and the mobile app (application) side. From the perspective of the integration of news and information, most of them are still in the form of manual editing and classified browsing. It will cause users to browse a large amount of news and information that are not of interest, waste the user's time, and the product itself also needs a large number of editors to update and maintain news and information; subscription news reading represented by google reader (Google Read)
  • the product is another product form different from the above products. Users can subscribe to the content of the website they are interested in to read and browse. This form of reading reduces the possibility of users browsing to content that is not of interest, but the user needs to find it by himself. The content and website that I am interested in make a series of settings, and for most Internet users, they don't like this cumbersome way.
  • the present invention has been made in order to provide a social network-based content recommendation method and system that overcomes the above problems or at least partially solves the above problems, and a news recommendation method and system.
  • a social network-based content recommendation method including: extracting features of social network data; calculating and recording the social network data according to behavior of the social network data by a certain type of user Feature for the user of the type of interest; extracting features of the plurality of to-be-pushed content; searching for the interest weight of the plurality of features to be pushed from the recorded features and interest weights, and calculating And extracting an interest score of the plurality of to-be-pushed content for the user of the type; and performing content push on the user of the type according to the level of interest of the plurality of to-be-pushed content for the user of the type.
  • a social network-based content recommendation system including: a first feature extraction module, configured to extract features of social network data; and an interest weight calculation module, configured to Calculating and recording, by a certain type of user, the behavior of the social network data, the feature weight of the feature of the social network data for the user of the type; the second feature extraction module, configured to extract a plurality of to-be-pushed content a feature score calculation module, configured to search for interest weights of the plurality of features to be pushed from the recorded features and interest weights, and calculate the plurality of to-be-pushed content for the type
  • the user's interest score; the content to be recommended module is configured to perform content push on the user of the type according to the level of the interest score of the plurality of to-be-pushed content for the type of user.
  • the social network-based content recommendation method and system of the present invention since the social behaviors of different types of users on the network can reflect the interests of the users of the type, the behaviors of the social network data based on different types of users are analyzed.
  • the technical solution of the present invention displays the recommended content to the user, greatly reduces the workload of the manual editing, improves the readability of the recommended content for the user, reduces the recommended content that the user does not like, and saves the user's time.
  • the increase in recommended quality will also drive more users, increase the click rate of recommended content, and ultimately lead to a steady increase in push traffic.
  • a news recommendation method including: extracting features of search query data; calculating and recording characteristics of the search query data according to behavior of the search query data by a certain type of user For the user's interest weight value; extracting a plurality of features to be pushed news; searching for the interest weights of the plurality of features to be pushed news from the recorded features and interest weights, and calculating the a plurality of interest scores of the users to be pushed for the type of users; and a news push for the users of the type according to the level of interest scores of the plurality of to-be-sent news for the types of users.
  • a news recommendation system including: a first feature extraction module, configured to extract features of the search query data; and an interest weight calculation module, configured to query the search data according to the user The behavior of calculating, and recording, the feature of the search query data for the user of the type of interest; the second feature extraction module for extracting features of the plurality of news to be pushed; the interest score calculation module, for Searching for the interest weights of the plurality of features to be pushed, and calculating the interest scores of the plurality of to-be-sent news for the user of the type; the news recommendation module is to be pushed, And performing news push on the user of the type according to the level of the interest score of the plurality of to-be-sent news for the type of user.
  • the characteristics of the search query data are analyzed based on the behavior of the search query data by different types of users.
  • the interest weights of different types of users, as well as the calculation of the interest scores of different types of users to be pushed by the news actually divide the interest of different types of users to push the news, and recommend news to different types of users according to the level of interest.
  • the technical scheme of the invention displays the news to the user, greatly reduces the workload of the manual editing, improves the readability of the news for the user, reduces the news that the user does not like, saves the user's time, and recommends the quality. Raising will also drive more users, increase the click-through rate of each news, and ultimately lead to a steady increase in news traffic.
  • a computer program comprising computer readable code
  • the computer readable code when run on a computing device, causes the computing device to perform a social network based content recommendation method and/or a news recommendation method as described above.
  • a computer readable medium storing the above computer program is provided.
  • FIG. 1 shows a flow chart of a social network based content recommendation method in accordance with one embodiment of the present invention
  • FIG. 2 shows a flow chart of a social network based content recommendation method in accordance with another embodiment of the present invention
  • FIG. 3 illustrates a workflow diagram of a social network based content recommendation method in accordance with one embodiment of the present invention
  • FIG. 4 shows a block diagram of a social network based content recommendation system in accordance with one embodiment of the present invention
  • FIG. 5 illustrates a block diagram of a social network based content recommendation system in accordance with another embodiment of the present invention
  • FIG. 6 shows a flow chart of a news recommendation method in accordance with one embodiment of the present invention
  • FIG. 7 shows a flowchart of a news recommendation method according to another embodiment of the present invention.
  • FIG. 8 is a flowchart showing the operation of a news recommendation method according to an embodiment of the present invention.
  • Figure 9 shows a block diagram of a news recommendation system in accordance with one embodiment of the present invention.
  • FIG. 10 shows a block diagram of a news recommendation system in accordance with another embodiment of the present invention.
  • FIG. 11 is a block diagram schematically showing a computing device for performing a social network based content recommendation method and/or a news recommendation method according to the present invention
  • Fig. 12 schematically shows a storage unit for holding or carrying program code implementing the social network based content recommendation method and/or news recommendation method according to the present invention.
  • an embodiment of the present invention provides a social network-based content recommendation method, including:
  • Step 110 extracting features of social network data.
  • the type of the social network data is not limited, for example, it may be a social networking site used by the user, a social tool, such as a microblog, a blog, etc., for example.
  • a social tool such as a microblog, a blog, etc.
  • it can be a social networking site, the name of a social tool, a category, a tag content, and the like.
  • Step 120 Calculate and record the interest weight of the feature of the social network data for the user of the above type according to the behavior of the social network data by a certain type of user. For example, users frequently send sports-like messages on social networks, which shows that users have a high interest in sports-like content.
  • Step 130 Extract features of multiple to-be-pushed content.
  • the content to be pushed in this embodiment includes, but is not limited to, news and information, or other forms of information.
  • Step 140 Search for the interest weights of the features of the plurality of to-be-pushed content from the recorded features and the interest weights, and calculate the interest scores of the plurality of to-be-pushed content for the users of the type.
  • the user's interest model may be established according to the foregoing characteristics of the social network data and the corresponding interest weight, and the candidate push content that needs to be pushed to the user may be selected through the interest model.
  • Step 150 Perform content push on the user of the above type according to the level of the interest score of the plurality of to-be-pushed content for the user of the above type.
  • the push content is sorted based on the interest score, and according to the sort result, the content set and the order to be recommended to the user may be determined.
  • the content is pushed based on the level of the interest score, that is, the interest of different types of users for the content to be pushed, which greatly reduces the workload of manual editing, and improves the recommended content for the user.
  • Readability reducing the number of recommended content that users don't like, saving users' time, improving the quality of recommendation will also drive more users, improve the click-through rate of each recommended content, and ultimately bring steadily increase the push traffic. .
  • another embodiment of the present invention further provides a social network-based content recommendation method, which further includes:
  • Step 160 Re-determine the interest scores of the plurality of to-be-pushed content according to the click behavior of the user of the above type to the plurality of to-be-pushed content.
  • Step 170 Calculate the interest weights of the features of the plurality of to-be-pushed content according to the re-determined interest score and record.
  • the push is accurate; but if the user clicks on a button that is not interested in the pushed content, the user indicates the classification or theme corresponding to the content.
  • the interest score of the content is estimated according to the actual behavior of the user, and the interest weight of the feature in the content is inversely corrected, so as to make the calculated interest score more consistent with the actual interest of the user later.
  • Another embodiment of the present invention further provides a social network-based content recommendation method, wherein the social network data includes a social network account, the characteristics of the social network data include categories and topics of the social network account, and the types of users are social
  • the behavior of network data includes the behavior of attention to social network accounts of the same category or the same subject.
  • the Weibo account can be set with corresponding categories, themes or other forms of labels; or at least one label can be set for different Weibo accounts in advance, and the characteristics of the Weibo account can be recorded in the label, and the label of the Weibo account can be stored. In the database, to extract when needed.
  • Another embodiment of the present invention further provides a social network-based content recommendation method, wherein the social network data includes social content published by a social network account, and the characteristics of the social network data include categories and topics of the social content, the above types User behavior of social network data includes forwarding behavior for social content of the same category or the same topic.
  • Another embodiment of the present invention further provides a social network-based content recommendation method, wherein the social network data includes a URL published by a social network account, and the feature of the social network data includes a category and a theme of the push content pointed to by the URL,
  • the type of user's behavior on social network data includes click behavior for URLs of push content for the same category or the same topic, or click behavior for page tags on push content for the same category or the same topic.
  • the category label may be set in advance for different push contents. For example, if the push content is sports information, the label is set as a sports label.
  • the category label of the domain name can be pre-stored in the database.
  • Another embodiment of the present invention further provides a social network-based content recommendation method, wherein the social network data includes a URL issued by a social network account, and the feature of the social network data includes a category of a domain name included in the URL, the above type
  • the behavior of the user on the social network data includes the click behavior of the URL corresponding to the domain name of the same category.
  • the category label may be set for different domain names in advance.
  • the category label of a domain name is usually the information category of the webpage included in the webpage under the domain name, such as sports.abc.com.
  • the web page may contain sports information in all aspects, and the category label of the domain name may be determined as "sports".
  • the category label of the domain name can be pre-stored in the database.
  • the user clicks on the news pointed to by the URL issued by a social account it indicates that the user is interested in the category and theme of the domain name, and may set a higher interest weight.
  • Another embodiment of the present invention further provides a social network-based content recommendation method, wherein an interest score of the i-th push-to-push content is:
  • V i x 1 ⁇ w 1 + x 2 ⁇ w 2 + ... + x N ⁇ w N , where w 1 ... w N is the N features of the i-th to be pushed content, x 1 ... x N is the weight of interest corresponding to N features, a is the first constant, b is the second constant, and e and g are fixed constants.
  • a ranking model can be implemented, and the model calculates the interest score using the above formula.
  • the sorting model is actually a logistic regression classifier.
  • the input of the logistic regression classifier is a feature of the push content.
  • the output is a piece of push content for a certain type of user's interest score. The higher the score indicates that the type of user belongs to the article. The more interesting the push content may be.
  • Each push content can be abstracted into a feature vector, and each dimension of the vector represents the theme, classification, and even the content of the push content. To multiple characteristics such as keywords and heat.
  • the logistic regression classifier used to calculate the push content interest value can be expressed as:
  • V XW
  • X represents the model coefficient vector corresponding to the user of the above type
  • W represents the feature vector of the push content
  • the meaning of the left side of the above equation is the possibility of the user clicking when recommending a push content news i to the user. Therefore, the calculated interest score on the right side can be used as a basis for pushing content of the above types of users.
  • a set of push content that the user clicked and a set of content that has been pushed to the user but the user has not clicked can be obtained.
  • the push content news c that the user clicked the following can be obtained:
  • V i x 1 ⁇ w 1 +x 2 ⁇ w 2 +...+x N ⁇ w N
  • P(Y 1
  • news i ) can be obtained by calculation.
  • This value is the user's interest score for this item.
  • the order of the content recommended to the user can be determined. It can be seen that, in the technical solution of the embodiment, the interest weight is corrected according to the actual click behavior of the user on the push content, which is beneficial to be more The content is pushed to the user accurately, and finally the embodiment is combined with the technical solution obtained by the foregoing embodiment, and the working flow thereof is shown in FIG. 3 .
  • another implementation of the present invention further provides a social network-based content recommendation system, including:
  • the first feature extraction module 410 is configured to extract features of the social network data.
  • the type of the social network data is not limited.
  • it may be a social networking site used by the user, a social tool, such as a microblog, a blog, etc., for example, may be a social networking site, a social tool name, a category, a label. Content, etc.
  • the interest weight calculation module 420 is configured to calculate and record the interest weight of the feature of the social network data for the user of the above type according to the behavior of the social network data by a certain type of user. For example, users frequently send sports-like messages on social networks, which shows that users have a high interest in sports-like content.
  • the second feature extraction module 430 is configured to extract a plurality of features of the content to be pushed.
  • the content to be pushed in this embodiment includes, but is not limited to, news and information, or other forms of information.
  • the interest score calculation module 440 is configured to search for the interest weights of the features of the plurality of to-be-pushed content from the recorded features and the interest weights, and calculate the interest scores of the plurality of to-be-pushed content for the users of the type.
  • the user's interest model may be established according to the foregoing characteristics of the social network data and the corresponding interest weight, and the candidate push content that needs to be pushed to the user may be selected through the interest model.
  • the content to be recommended module 450 is configured to perform content push on the user of the above type according to the level of interest scores of the plurality of to-be-pushed content for the user of the above type.
  • the push content is sorted based on the interest score, and according to the sort result, the content set and the order to be recommended to the user may be determined.
  • the content is pushed based on the level of the interest score, that is, the interest of different types of users for the content to be pushed, which greatly reduces the workload of manual editing, and improves the recommended content for the user.
  • Readability reducing the number of recommended content that users don't like, saving users' time, improving the quality of recommendation will also drive more users, improve the click-through rate of each recommended content, and ultimately bring steadily increase the push traffic. .
  • another embodiment of the present invention further provides a social network-based content recommendation system, which further includes:
  • the first re-determination module 460 is configured to re-determine the interest scores of the plurality of to-be-pushed content according to the click behavior of the user to the plurality of to-be-pushed content.
  • the second re-determination module 470 is configured to calculate and record the interest weights of the features of the plurality of to-be-pushed content according to the re-determined interest score.
  • the push is accurate; but if the user clicks on a button that is not interested in the pushed content, the user indicates the classification or theme corresponding to the content.
  • the interest score of the content is estimated according to the actual behavior of the user, and the interest weight of the feature in the content is inversely corrected, so as to make the calculated interest score more consistent with the actual interest of the user later.
  • Another embodiment of the present invention further provides a social network-based content recommendation system, wherein the social network data includes a social network account, the characteristics of the social network data include categories and topics of the social network account, and the types of users are social
  • the behavior of network data includes the behavior of attention to social network accounts of the same category or the same subject.
  • the Weibo account can be set with corresponding categories, themes or other forms of labels; or at least one label can be set for different Weibo accounts in advance, and the characteristics of the Weibo account can be recorded in the label, and the label of the Weibo account can be stored. In the database, to extract when needed.
  • Another embodiment of the present invention further provides a social network-based content recommendation system, wherein the social network data includes social content published by a social network account, and the characteristics of the social network data include categories and topics of the social content, the above types User behavior of social network data includes forwarding behavior for social content of the same category or the same topic.
  • Another embodiment of the present invention further provides a social network-based content recommendation system, wherein the social network data includes a URL published by a social network account, and the feature of the social network data includes a category and a theme of the push content pointed to by the URL,
  • the type of user's behavior on social network data includes click behavior for URLs of push content for the same category or the same topic, or click behavior for page tags on push content for the same category or the same topic.
  • the category label may be set in advance for different push contents. For example, if the push content is sports information, the label is set as a sports label.
  • the category label of the domain name can be pre-stored in the database.
  • Another embodiment of the present invention further provides a social network-based content recommendation system, wherein the social network data includes a URL issued by a social network account, and the feature of the social network data includes a category of a domain name included in the URL, the above type
  • the behavior of the user on the social network data includes the click behavior of the URL corresponding to the domain name of the same category.
  • the category label may be set for different domain names in advance.
  • the category label of a domain name is usually the information category of the webpage included in the webpage under the domain name, such as sports.abc.com.
  • the web page may contain sports information in all aspects, and the category label of the domain name may be determined as "sports".
  • the category label of the domain name can be pre-stored in the database.
  • the user clicks on the news pointed to by the URL issued by a social account it indicates that the user is interested in the category and theme of the domain name, and may set a higher interest weight.
  • Another embodiment of the present invention further provides a social network-based content recommendation system, wherein the interest score of the i-th push-to-push content is:
  • V i x 1 ⁇ w 1 + x 2 ⁇ w 2 + ... + x N ⁇ w N , where w 1 ... w N is the N features of the i-th to be pushed content, x 1 ... x N is the weight of interest corresponding to N features, a is the first constant, b is the second constant, and e and g are fixed constants.
  • a sorting model can be implemented.
  • the type uses the above formula to calculate the interest score.
  • the sorting model is actually a logistic regression classifier.
  • the input of the logistic regression classifier is a feature of the push content.
  • the output is a piece of push content for a certain type of user's interest score. The higher the score indicates that the type of user belongs to the article. The more interesting the push content may be.
  • Each piece of push content can be abstracted into a feature vector, and each dimension of the vector represents a plurality of features such as a theme, a classification, and even a keyword, a heat, and the like of the piece of push content.
  • the logistic regression classifier used to calculate the push content interest value can be expressed as:
  • V XW
  • X represents the model coefficient vector corresponding to the user of the above type
  • W represents the feature vector of the push content
  • the meaning of the left side of the above equation is the possibility of the user clicking when recommending a push content news i to the user. Therefore, the calculated interest score on the right side can be used as a basis for pushing content of the above types of users.
  • a set of push content that the user clicked and a set of content that has been pushed to the user but the user has not clicked can be obtained.
  • the push content news c that the user clicked the following can be obtained:
  • V i x 1 ⁇ w 1 +x 2 ⁇ w 2 +...+x N ⁇ w N
  • P(Y 1
  • news i ) can be obtained by calculation.
  • This value is the user's interest score for this item.
  • the order of the content recommended to the user can be determined. It can be seen that, in the technical solution of the embodiment, the interest weight is corrected according to the actual click behavior of the user on the push content, which is beneficial to be more The content is pushed to the user accurately, and finally the embodiment is combined with the technical solution obtained by the foregoing embodiment, and the working flow thereof is shown in FIG. 3 .
  • an embodiment of the present invention provides a news recommendation method, including:
  • Step 610 extracting features of the search query data.
  • the type of the search query data is not limited, for example, it may be a browsing situation of the user's search for the news, etc.; the embodiment does not limit the characteristics of the search query data, for example, may be browsed by the user.
  • Step 620 Calculate and record the interest weight of the feature of the search query data for the user of the type according to the behavior of the search query data by a certain type of user. For example, for browsing behavior, the user is inevitably interested in the news of first browsing and repeating browsing, thereby analyzing the user's interest weight.
  • step 630 a plurality of features of the news to be pushed are extracted.
  • Step 640 Search for the interest weights of the plurality of features to be pushed from the recorded features and the interest weights, and calculate the interest scores of the plurality of to-be-sent news for the users of the above type.
  • the user's interest model can be established according to the characteristics of the foregoing search query data and the corresponding interest weight, and the candidate news that needs to be pushed to the user can be selected through the interest model.
  • Step 650 Push the user according to the level of interest scores of the plurality of users to be pushed for the above type of news.
  • the push news is sorted based on the interest score, and according to the sort result, the news set and the order to be recommended to the user may be determined.
  • the news is pushed based on the level of the interest score, that is, the interest of different types of users for the news to be pushed, which greatly reduces the workload of manual editing, and improves the news for the user.
  • Readability reduces the amount of news that users don't like, saves users' time, and improves the quality of recommendation. It also drives more users, improves the click-through rate of each news, and ultimately leads to a steady increase in news traffic.
  • another embodiment of the present invention further provides a news recommendation method, which further includes:
  • Step 660 Re-determine the interest scores of the plurality of news to be pushed according to the click behavior of the plurality of users to push the news according to the above type.
  • Step 670 Calculate the interest weights of the features of the plurality of news to be pushed and record according to the re-determined interest score.
  • the push is accurate; but if the user clicks on a button that is not interested in the pushed news, the user indicates the classification or theme corresponding to the news.
  • the feature has a low interest, at this time, the interest score of the news is estimated according to the actual behavior of the user, and the interest weight of the feature of the news is inversely modified, so that the calculated interest score is more consistent with the actual interest of the user in the future. .
  • Another embodiment of the present invention further provides a news recommendation method, wherein the search query data includes a query word, the characteristics of the search query data include a category and a topic of the query word, and the behavior of the user of the above type to the search query data includes Query behavior for query terms of the same category or the same subject.
  • the category label and the topic label of the query word may be determined according to the category label and the topic label of the news in the news set corresponding to the query word, and the database is stored for storage, and the category of the query word and Themes can be extracted from the category tags and theme tags in the database.
  • the search query word abc the most popular topic tag in the news is t1
  • the topic tag corresponding to the query word is t1
  • the category tag with the most news is c1
  • the category tag corresponding to the query word is c1.
  • the difference in the query behavior of the query word by the user mainly includes: different search frequencies and different search times.
  • Another embodiment of the present invention further provides a news recommendation method, wherein the search query data includes a URL on a query result page, and the feature of the search query data includes a category and a topic of the news pointed to by the URL, and the user type search of the above type
  • the behavior of querying data includes click behavior on URLs of news of the same category or the same topic, or click behavior on page tags on news of the same category or the same topic.
  • a category label and at least one theme label may be set in advance for each news, and one category and at least one theme of the news are recorded therein.
  • the user when the user clicks and reads the news pointed to by the searched URL, the user indicates that the user is interested in the category and theme of the news, and may set a higher interest weight; or, the user clicks A news classification channel pointed to by a certain URL, and the news of the classified channel has the same category label, indicating that the user is interested in the category of the news, and a higher interest weight may be set.
  • Another embodiment of the present invention further provides a news recommendation method, wherein the search query data includes a URL issued by a social network account, and the feature of the search query data includes a category of a domain name included in the URL, and the user of the above type searches for the query data.
  • the behavior includes click behavior on URLs corresponding to domain names of the same category.
  • the category label may be set for different domain names in advance.
  • the category label of a domain name is usually the information category of the webpage included in the webpage under the domain name, such as sports.abc.com.
  • the web page may contain sports information in all aspects, and the category label of the domain name may be determined as "sports".
  • the category label of the domain name can be pre-stored in the database.
  • the user searches for the URL published by a social account and clicks to read the news pointed to by the URL, indicating that the user is interested in the category and topic of the domain name, and may set a higher interest weight. .
  • Another embodiment of the present invention further provides a news recommendation method, wherein the interest score of the i-th push to be pushed is:
  • V i x 1 ⁇ w 1 + x 2 ⁇ w 2 + ... + x N ⁇ w N , where w 1 ... w N is the N features of the i-th news to be pushed, x 1 ... x N is the weight of interest corresponding to N features, a is the first constant, b is the second constant, and e and g are fixed constants.
  • a ranking model can be implemented, and the model calculates the interest score using the above formula.
  • the sorting model is actually a logistic regression classifier.
  • the input of the logistic regression classifier is a feature of the news.
  • the output is a news score for a certain type of user. The higher the score indicates that the type of user may be interested in this news. The more interested. Every news can be abstracted into one
  • the feature vector each dimension of the vector represents the subject, classification, and even keywords, heat, and other characteristics of the news.
  • the logistic regression classifier used for the calculation of news interest value can be expressed as:
  • V XW
  • X represents the model coefficient vector corresponding to the user of the above type
  • W represents the feature vector of the news
  • the meaning of the left side of the above equation is the possibility of the user clicking when recommending a news news i to the user, so
  • the calculated interest score on the right side can be used as a basis for pushing news of the above types of users.
  • a news collection that the user clicked and a batch of news collections that have been pushed to the user but are not clicked by the user can be obtained.
  • the news news c that the user clicked the following can be obtained:
  • V i x 1 ⁇ w 1 +x 2 ⁇ w 2 +...+x N ⁇ w N
  • P(Y 1
  • news i ) can be obtained by calculation.
  • This value is the user's interest score for this news.
  • the order of recommending the news to the user can be determined. It can be seen that the technical solution of the embodiment corrects the interest weight according to the actual click behavior of the user to push the news, which is beneficial to be more accurate again. The user pushes the news to the user, and finally the embodiment is combined with the technical solution obtained by the foregoing embodiment, and the working flow thereof is as shown in FIG. 8 .
  • each of the above formulas is not the only formula for implementing the present invention, and is merely an implementation of the embodiment.
  • the technician can appropriately deform the formula according to the business needs, and still fall in the invention. Within the range, for example, adding parameters or multiple values.
  • another embodiment of the present invention further provides a news recommendation system, including:
  • the first feature extraction module 910 is configured to extract features of the search query data.
  • the type of the search query data is not limited, for example, it may be a browsing situation of the user's search for the news, etc.; the embodiment does not limit the characteristics of the search query data, for example, may be browsed by the user.
  • the interest weight calculation module 920 is configured to calculate and record the interest weight of the feature of the search query data for the user of the above type according to the behavior of the user on the search query data. For example, for browsing behavior, the user is inevitably interested in the news of first browsing and repeating browsing, thereby analyzing the user's interest weight.
  • the second feature extraction module 930 is configured to extract a plurality of features of the news to be pushed.
  • the interest score calculation module 940 is configured to search for the interest weights of the plurality of features to be pushed from the recorded features and the interest weights, and calculate the interest scores of the plurality of to-be-sent news for the users of the above type.
  • the user's interest model can be established according to the characteristics of the foregoing search query data and the corresponding interest weight, and the candidate news that needs to be pushed to the user can be selected through the interest model.
  • the news recommendation module 950 is configured to push a plurality of news to be pushed to the users of the above type in order according to the level of interest scores of the plurality of users to be pushed for the above-mentioned types of news.
  • the push news is sorted based on the interest score, and according to the sort result, the news set and the order to be recommended to the user may be determined.
  • the news is pushed based on the level of the interest score, that is, the interest of different types of users for the news to be pushed, which greatly reduces the workload of manual editing, and improves the news for the user.
  • Readability reduces the amount of news that users don't like, saves users' time, and improves the quality of recommendation. It also drives more users, improves the click-through rate of each news, and ultimately leads to a steady increase in news traffic.
  • another embodiment of the present invention further provides a news recommendation system, which further includes:
  • the first re-determination module 960 is configured to re-determine the interest scores of the plurality of news to be pushed according to the click behavior of the plurality of users to push the news according to the above type.
  • the second re-determination module 970 is configured to calculate, according to the re-determined interest score, the interest weights of the features of the plurality of news to be pushed and record.
  • the push is accurate; but if the user clicks on a button that is not interested in the pushed news, the user indicates the classification or theme corresponding to the news.
  • the feature has a lower interest, at which time the interest score of the news is estimated based on the actual behavior of the user, and the interest weight of the feature of the news is inversely modified so as to make the calculated interest score more consistent with the actual interest of the user.
  • Another embodiment of the present invention further provides a news recommendation system, wherein the search query data includes a query word, the characteristics of the search query data include a category and a topic of the query word, and the behavior of the user of the above type to the search query data includes Query behavior for query terms of the same category or the same subject.
  • the category label and the topic label of the query word may be determined according to the category label and the topic label of the news in the news set corresponding to the query word, and the database is stored for storage, and the category of the query word and Themes can be extracted from the category tags and theme tags in the database.
  • search The query word abc the most popular topic tag in the news is t1
  • the topic tag corresponding to the query word is t1
  • the category tag with the most news is c1
  • T1 and c1 are extracted as features of the categories and topics of the query words.
  • the difference in the query behavior of the query word by the user mainly includes: different search frequencies and different search times.
  • Another embodiment of the present invention further provides a news recommendation system, wherein the search query data includes a URL on a query result page, the feature of the search query data includes a category of news pointed to by the URL, and the user of the above type pairs the search query data
  • the behavior includes click behavior on URLs of news of the same category, or click behavior on page tags on news of the same category or the same topic.
  • a category label and at least one theme label may be set in advance for each news, and one category and at least one theme of the news are recorded therein.
  • the user when the user clicks and reads the news pointed to by the searched URL, the user indicates that the user is interested in the category and theme of the news, and may set a higher interest weight; or, the user clicks A news classification channel pointed to by a certain URL, and the news of the classified channel has the same category label, indicating that the user is interested in the category of the news, and a higher interest weight may be set.
  • Another embodiment of the present invention further provides a news recommendation system, wherein the search query data includes a URL published by a social network account, and the feature of the search query data includes a category and a topic of a domain name included in the URL, and the user pair of the above type
  • the behavior of searching for query data includes click behavior for URLs corresponding to domain names of the same category or the same topic.
  • the category label may be set for different domain names in advance.
  • the category label of a domain name is usually the information category of the webpage included in the webpage under the domain name, such as sports.abc.com.
  • the web page may contain sports information in all aspects, and the category label of the domain name may be determined as "sports".
  • the category label of the domain name can be pre-stored in the database.
  • the user searches for the URL published by a social account and clicks to read the news pointed to by the URL, indicating that the user is interested in the category and topic of the domain name, and may set a higher interest weight. .
  • Another embodiment of the present invention further provides a news recommendation system, wherein the interest score of the i-th push to be pushed is:
  • V i x 1 ⁇ w 1 + x 2 ⁇ w 2 + ... + x N ⁇ w N , where w 1 ... w N is the N features of the i-th news to be pushed, x 1 ... x N is the weight of interest corresponding to N features, a is the first constant, b is the second constant, and e and g are fixed constants.
  • a sorting model can be implemented, and the model is advantageous.
  • the interest score is calculated using the above formula.
  • the sorting model is actually a logistic regression classifier.
  • the input of the logistic regression classifier is a feature of the news.
  • the output is a news score for a certain type of user. The higher the score indicates that the type of user may be interested in this news. The more interested.
  • Each piece of news can be abstracted into a feature vector. Each dimension of the vector represents the topic, classification, and even keywords, heat, and other characteristics of the news.
  • the logistic regression classifier used for the calculation of news interest value can be expressed as:
  • V XW
  • X represents the model coefficient vector corresponding to the user of the above type
  • W represents the feature vector of the news
  • the meaning of the left side of the above equation is the possibility of the user clicking when recommending a news news i to the user, so
  • the calculated interest score on the right side can be used as a basis for pushing news of the above types of users.
  • a news collection that the user clicked and a batch of news collections that have been pushed to the user but are not clicked by the user can be obtained.
  • the news news c that the user clicked the following can be obtained:
  • V i x 1 ⁇ w 1 +x 2 ⁇ w 2 +...+x N ⁇ w N
  • P(Y 1
  • news i ) can be obtained by calculation.
  • This value is the user's interest score for this news.
  • the order of recommending the news to the user can be determined. It can be seen that the technical solution of the embodiment corrects the interest weight according to the actual click behavior of the user to push the news, which is beneficial to be more accurate again. The user pushes the news to the user, and finally the embodiment is combined with the technical solution obtained by the foregoing embodiment, and the working flow thereof is as shown in FIG. 8 .
  • modules in the devices of the embodiments can be adaptively changed and placed in one or more devices different from the embodiment.
  • the modules or units or components of the embodiments may be combined into one module or unit or component, and further they may be divided into a plurality of sub-modules or sub-units or sub-components.
  • any combination of the features disclosed in the specification, including the accompanying claims, the abstract and the drawings, and any methods so disclosed, or All processes or units of the device are combined.
  • Each feature disclosed in this specification (including the accompanying claims, the abstract and the drawings) may be replaced by alternative features that provide the same, equivalent or similar purpose.
  • the various component embodiments of the present invention may be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof.
  • a microprocessor or digital signal processor can be used in practice to implement a social network based content recommendation system in accordance with embodiments of the present invention, as well as some or all of the components of the news recommendation system. Some or all of the features.
  • the invention can also be implemented as a device or device program (e.g., a computer program and a computer program product) for performing some or all of the methods described herein.
  • Such a program implementing the invention may be stored on a computer readable medium or may be in the form of one or more signals. Such signals may be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.
  • Figure 11 illustrates a computing device that can implement a method of transferring data between smart terminals.
  • the computing device conventionally includes a processor 1110 and a computer program product or computer readable medium in the form of a memory 1120.
  • the memory 1120 may be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read Only Memory), an EPROM, a hard disk, or a ROM.
  • Memory 1120 has a memory space 1130 for program code 1131 for performing any of the method steps described above.
  • the storage space 1130 for program code may include respective program codes 1131 for implementing various steps in the above methods, respectively.
  • the program code can be read from or written to one or more computer program products.
  • These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks.
  • Such a computer program product is typically a portable or fixed storage unit as described with reference to FIG.
  • the storage unit may have a storage segment, a storage space, and the like that are similarly arranged to the storage 1120 in the computing device of FIG.
  • the program code can be compressed, for example, in an appropriate form.
  • the element includes computer readable code 1131', i.e., code readable by a processor, such as 1110, that when executed by a computing device causes the computing device to perform various steps in the methods described above.

Landscapes

  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Business, Economics & Management (AREA)
  • General Engineering & Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • Operations Research (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Quality & Reliability (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Computing Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Information Transfer Between Computers (AREA)

Abstract

本发明提供了一种基于社交网络的内容推荐方法和系统，以及一种新闻推荐方法和系统。其中，基于社交网络的内容推荐方法包括：提取社交网络数据的特征；根据某一类型的用户对所述社交网络数据的行为，计算并记录所述社交网络数据的特征对于所述类型的用户的兴趣权值；提取多个待推送内容的特征；从已记录的特征及兴趣权值中，查找所述多个待推送内容的特征的兴趣权值，并计算出所述多个待推送内容对于所述类型的用户的兴趣得分；根据所述多个待推送内容对于所述类型的用户的兴趣得分的高低，对所述类型的用户进行内容推送。通过本发明，可以分析不同类型用户的兴趣所在，并将符合用户兴趣的内容推送给用户。

Description

基于社交网络的内容、新闻推荐方法和系统 技术领域
本发明涉及信息技术领域，特别是一种基于社交网络的内容推荐方法和系统，以及一种新闻推荐方法和系统。
背景技术
获取新闻和资讯是现代社会中人们的生活习惯，随着计算机技术的发展和互联网用户规模的不断扩大，越来越多的人使用通过互联网获得各种各样所需的信息。同时，通过互联网提供新闻和资讯服务的网站也越来越多，越来越多的突发新闻和事件是通过互联网得到迅速传播的，互联网信息呈现出爆发式的增长趋势。近些年来，移动互联网的快速发展使得用户的阅读时间变得越来越碎片化，在这种背景下，如何在海量的信息中筛选出最有价值的信息，向用户个性化推荐其最感兴趣的新闻和资讯，就变得极其重要。
现在的互联网新闻资讯阅读产品主要包括web(网页)端和移动app(应用程序)端，从新闻和资讯的整合方式来看，大部分依然是人工编辑和分类浏览的形式，这种方式的阅读会使用户浏览到大量不感兴趣的新闻和资讯，浪费用户的时间，同时产品本身也需要大量的编辑来进行新闻和资讯的更新和维护；以google reader(谷歌阅读)为代表的订阅类新闻阅读产品是不同于上述产品的另外一种产品形式，用户可以订阅自己感兴趣的网站的内容进行阅读和浏览，这种阅读形式减少了用户浏览到不感兴趣的内容的可能性，但是用户需要自己寻找自己感兴趣的内容和网站进行一系列设置，而对于大多数互联网用户来说，他们不喜欢这种繁琐的方式。
为了使得用户在最短的时间内以最便捷的方式获取最有价值和最感兴趣的新闻和资讯，必须采取一种更加智能的方式去提供给用户所需的信息，针对不同的用户推荐用户最感兴趣和最有价值的新闻和资讯。
发明内容
鉴于上述问题，提出了本发明以便提供一种克服上述问题或者至少部分地解决上述问题的基于社交网络的内容推荐方法和系统，以及新闻推荐方法和系统。
根据本发明的一方面，提供了基于社交网络的内容推荐方法，其包括：提取社交网络数据的特征；根据某一类型的用户对所述社交网络数据的行为，计算并记录所述社交网络数据的特征对于所述类型的用户的兴趣权值；提取多个待推送内容的特征；从已记录的特征及兴趣权值中，查找所述多个待推送内容的特征的兴趣权值，并计算出所述多个待推送内容对于所述类型的用户的兴趣得分；根据所述多个待推送内容对于所述类型的用户的兴趣得分的高低，对所述类型的用户进行内容推送。
根据本发明的另一方面，还提供了一种基于社交网络的内容推荐系统，其包括：第一特征提取模块，用于提取社交网络数据的特征；兴趣权值计算模块，用于根据 某一类型的用户对所述社交网络数据的行为，计算并记录所述社交网络数据的特征对于所述类型的用户的兴趣权值；第二特征提取模块，用于提取多个待推送内容的特征；兴趣得分计算模块，用于从已记录的特征及兴趣权值中，查找所述多个待推送内容的特征的兴趣权值，并计算出所述多个待推送内容对于所述类型的用户的兴趣得分；内容待推荐模块，用于根据所述多个待推送内容对于所述类型的用户的兴趣得分的高低，对所述类型的用户进行内容推送。
在本发明的基于社交网络的内容推荐方法和系统中，由于不同类型用户在网络上的社交行为，能够反映对该类型用户的兴趣所在，所以基于不同类型用户对社交网络数据的行为，分析得到社交网络数据特征对于不同类型用户的兴趣权值，以及计算出待推送内容对不同类型用户的兴趣得分，实际上是合理分出了不同类型用户对待推送内容的兴趣高低，按兴趣高低对不同类型的用户进行推荐。本发明的技术方案给用户展示推荐内容，大大减少了人工编辑的工作量，对用户而言，提升了推荐内容的可读性，减少了大量用户不喜欢的推荐内容，节约了用户的时间，推荐质量的提高也会带动更多的用户，提高了推荐内容的点击率，最终带来推送流量的稳步提升。
根据本发明的又一方面，提供了一种新闻推荐方法，其包括：提取搜索查询数据的特征；根据某一类型用户对所述搜索查询数据的行为，计算并记录所述搜索查询数据的特征对于所述类型用户的兴趣权值；提取多个待推送新闻的特征；从已记录的特征及兴趣权值中，查找所述多个待推送新闻的特征的兴趣权值，并计算出所述多个待推送新闻对于所述类型的用户的兴趣得分；根据所述多个待推送新闻对于所述类型的用户的兴趣得分的高低，对所述类型的用户进行新闻推送。
根据本发明的又一方面，提供了一种新闻推荐系统，其包括：第一特征提取模块，用于提取搜索查询数据的特征；兴趣权值计算模块，用于根据用户对所述搜索查询数据的行为，计算并记录所述搜索查询数据的特征对于所述类型的用户的兴趣权值；第二特征提取模块，用于提取多个待推送新闻的特征；兴趣得分计算模块，用于从已记录的特征及兴趣权值中，查找所述多个待推送新闻的特征的兴趣权值，并计算出所述多个待推送新闻对于所述类型的用户的兴趣得分；待推送新闻推荐模块，用于根据所述多个待推送新闻对于所述类型的用户的兴趣得分的高低，对所述类型的用户进行新闻推送。
在本发明的新闻推荐方法和系统中，由于不同类型用户对搜索查询数据的行为，能够反映对该类型用户的兴趣所在，所以基于不同类型用户对搜索查询数据行为，分析得到搜索查询数据特征对于不同类型用户的兴趣权值，以及计算出待推送新闻对不同类型用户的兴趣得分，实际上是合理分出了不同类型用户对待推送新闻的兴趣高低，按兴趣高低对不同类型的用户推荐新闻。本发明的技术方案给用户展示新闻，大大减少了人工编辑的工作量，对用户而言，提升了新闻的可读性，减少了大量用户不喜欢的新闻，节约了用户的时间，推荐质量的提高也会带动更多的用户，提高了每条新闻的点击率，最终带来新闻流量的稳步提升。
根据本发明的又一方面，提供了一种计算机程序，其包括计算机可读代码，当 所述计算机可读代码在计算设备上运行时，导致所述计算设备执行根据上文所述的基于社交网络的内容推荐方法和/或新闻推荐方法。
根据本发明的再一方面，提供了一种计算机可读介质，其中存储了上述的计算机程序。
上述说明仅是本发明技术方案的概述，为了能够更清楚了解本发明的技术手段，而可依照说明书的内容予以实施，并且为了让本发明的上述和其它目的、特征和优点能够更明显易懂，以下特举本发明的具体实施方式。
附图说明
通过阅读下文优选实施方式的详细描述，各种其他的优点和益处对于本领域普通技术人员将变得清楚明了。附图仅用于示出优选实施方式的目的，而并不认为是对本发明的限制。而且在整个附图中，用相同的参考符号表示相同的部件。在附图中：
图1示出了根据本发明的一个实施例的基于社交网络的内容推荐方法的流程图；
图2示出了根据本发明的另一个实施例的基于社交网络的内容推荐方法的流程图；
图3示出了根据本发明的一个实施例的基于社交网络的内容推荐方法的工作流程图；
图4示出了根据本发明的一个实施例的基于社交网络的内容推荐系统的框图；
图5示出了根据本发明的另一个实施例的基于社交网络的内容推荐系统的框图；
图6示出了根据本发明的一个实施例的新闻推荐方法的流程图；
图7示出了根据本发明的另一个实施例的新闻推荐方法的流程图；
图8示出了根据本发明的一个实施例的新闻推荐方法的工作流程图；
图9示出了根据本发明的一个实施例的新闻推荐系统的框图；
图10示出了根据本发明的另一个实施例的新闻推荐系统的框图；
图11示意性地示出了用于执行根据本发明的基于社交网络的内容推荐方法和/或新闻推荐方法的计算设备的框图；以及
图12示意性地示出了用于保持或者携带实现根据本发明的基于社交网络的内容推荐方法和/或新闻推荐方法的程序代码的存储单元。
具体实施方式
下面结合附图和具体的实施方式对本发明作进一步的描述。
如图1所示，本发明的一个实施例提供了一种基于社交网络的内容推荐方法，其包括：
步骤110，提取社交网络数据的特征。本实施例中对于社交网络数据的类型不做限定，例如，可以是用户所使用的社交网站、社交工具，例如微博、博客等，例 如，可以是社交网站、社交工具的名称、分类、标签内容等。
步骤120，根据某一类型的用户对社交网络数据的行为，计算并记录社交网络数据的特征对于上述类型的用户的兴趣权值。例如，用户在社交网络上频繁发出体育类消息，由此可见用户对体育类内容具有较高的兴趣。
步骤130，提取多个待推送内容的特征。本实施例中的待推送内容包括但不限于新闻以及资讯，或其他形式的信息。
步骤140，从已记录的特征及兴趣权值中，查找多个待推送内容的特征的兴趣权值，并计算出多个待推送内容对于上述类型的用户的兴趣得分。在本实施例的技术方案中，依据前述的社交网络数据的特征以及相应的兴趣权值可以建立用户的兴趣模型，通过兴趣模型可以选择出需要推送给用户的候选推送内容。
步骤150，根据多个待推送内容对于上述类型的用户的兴趣得分的高低，对上述类型的用户进行内容推送。本实施例中，基于兴趣得分对待推送内容进行排序，根据排序结果可以确定最终要推荐给用户的内容集合以及顺序。
在本实施例的技术方案中，基于兴趣得分的高低，也即不同类型用户对于待推送内容的兴趣高低进行内容推送，大大减少了人工编辑的工作量，对用户而言，提升了推荐内容的可读性，减少了大量用户不喜欢的推荐内容，节约了用户的时间，推荐质量的提高也会带动更多的用户，提高了每条推荐内容的点击率，最终带来推送流量的稳步提升。
如图2所示，本发明的另一实施例还提供了一种基于社交网络的内容推荐方法，其中，还包括：
步骤160，根据上述类型的用户对多个待推送内容的点击行为，重新确定多个待推送内容的兴趣得分。
步骤170，按重新确定的兴趣得分，计算多个待推送内容的特征的兴趣权值并进行记录。
在本实施例的技术方案中，如果用户点击阅读了推送内容，说明推送准确；但如用户对推送的某条内容点击了不感兴趣的按钮，表示用户对于该内容所对应的分类或主题等特征具有较低兴趣，此时根据用户的实际行为估算该内容的兴趣得分，并反向修正该内容中的特征的兴趣权值，以便于在以后使得计算的兴趣得分与用户的实际兴趣更符合。
本发明的另一实施例还提供了一种基于社交网络的内容推荐方法，其中，社交网络数据包括社交网络账号，社交网络数据的特征包括社交网络账号的类别和主题，上述类型的用户对社交网络数据的行为包括对相同类别或相同主题的社交网络账号的关注行为。
在本实施例的技术方案中，以目前流行的微博为例，如用户关注某一媒体账号或者名人账号，则表示用户对该类型或主题的微博账号感兴趣，用户对同一标签下的微博账号关注的越多，则可设置较高的兴趣权值。目前微博账号均可设置对应的类别、主题或是其他形式的标签；也可以预先为不同微博账号制定至少一个标签，并在标签中记录微博账号的特征，微博账号的标签可以存储在数据库中，以在需要时进行提取。
本发明的另一实施例还提供了一种基于社交网络的内容推荐方法，其中，社交网络数据包括社交网络账号发布的社交内容，社交网络数据的特征包括社交内容的类别和主题，上述类型的用户对社交网络数据的行为包括对相同类别或相同主题的社交内容的转发行为。
在本实施例的技术方案中，以目前流行的微博发出的正文为例，如用户对某一类别或主题的微博账号的正文的转发次数越多，则表示用户对该类别或主题的微博账号的正文具有较高的兴趣，则可以设置较高的兴趣权值。
本发明的另一实施例还提供了一种基于社交网络的内容推荐方法，其中，社交网络数据包括社交网络账号发布的URL，社交网络数据的特征包括URL指向的推送内容的类别和主题，上述类型的用户对社交网络数据的行为包括对相同类别或相同主题的推送内容的URL的点击行为，或对相同类别或相同主题的推送内容上的页面标签的点击行为。
在本实施例的技术方案中，可以预先为不同推送内容设置类别标签，例如，如果推送内容为体育资讯，则其标签设置为体育标签。域名的类别标签可预先存储在数据库中。在本实施例的技术方案中，用户点击了某社交账号发布的URL指向的新闻，则表示用户对于该域名的类别和主题感兴趣，则可以设置较高的兴趣权值。
本发明的另一实施例还提供了一种基于社交网络的内容推荐方法，其中，社交网络数据包括社交网络账号发布的URL，社交网络数据的特征包括URL中包含的域名的类别，上述类型的用户对社交网络数据的行为包括对相同类别的域名对应的URL的点击行为。
在本实施例的技术方案中，可以预先为不同域名设置类别标签，例如，一个域名的类别标签通常是这个域名下的网页所包含的网页的信息类别，比如sports.abc.com，其下的网页可能包含了各个方面的体育信息，则可以把此域名的类别标签确定为“体育”。域名的类别标签可预先存储在数据库中。
在本实施例的技术方案中，用户点击了某社交账号发布的URL指向的新闻，则表示用户对于该域名的类别和主题感兴趣，则可以设置较高的兴趣权值。
本发明的另一实施例还提供了一种基于社交网络的内容推荐方法，其中，第i个待推送内容的兴趣得分为：
Figure PCTCN2015082282-appb-000001
其中，Vi＝x1×w1+x2×w2+…+xN×wN，其中，w1……wN为第i个待推送内容的N个特征，x1……xN为对应N个特征的兴趣权值，a为第一常数，b为第二常数，e、g均为固定常数。
在本实施例的技术方案中，基于上述的得分公式，可以实现一排序模型，该模型利用上述公式计算兴趣得分。排序模型实际上是一个逻辑回归分类器，该逻辑回归分类器的输入是一条推送内容的特征，输出是一条推送内容针对某一类型的用户的兴趣得分，得分越高表示该类型用户对这条推送内容可能越感兴趣。每条推送内容可以抽象为一个特征向量，向量的每个维度表示该条推送内容的主题、分类，甚 至关键词、热度等多个特征。
假设我们已经根据上述的兴趣权值得到模型系数向量为X＝{x1,x2,…,xN}，则可将用来进行推送内容兴趣值计算的逻辑回归分类器表示为：
Figure PCTCN2015082282-appb-000002
其中，V＝XW，X表示上述类型的用户对应的模型系数向量，W表示推送内容的特征向量，上述等式的左边的意义是当向用户推荐一条推送内容newsi时，用户点击的可能性，所以计算得到的右边的兴趣得分可以作为对上述类型用户推送内容的依据。
结合前述的实施例，在用户对推送内容进行处理的情况下，W已知，X未知，求X。
根据用户的点击行为的反馈，可以得到用户点击过的推送内容集合和一批向用户推送过但是用户没有点击的内容集合，对于用户点击过的推送内容newsc，可以得到：
Figure PCTCN2015082282-appb-000003
对于用户没有点击过的推送内容newsd，可以得到：
Figure PCTCN2015082282-appb-000004
这样根据一个用户对m条推送内容点击记录，我们就得到了m个形式如上所述两个表达式的式子，联立求解，即可得到该用户的排序模型系数向量X，也即修正了兴趣权值。
在兴趣权值修正之后，设模型系数向量为{x1,x2,…,xN}，将候选的推送内容集合中的每一条推送内容提取得到对应的特征向量Wi＝{w1,w2,…,wN}，带入到模型中：
Figure PCTCN2015082282-appb-000005
其中，Vi＝x1×w1+x2×w2+…+xN×wN，计算则可得到P(Y＝1|newsi)。这个值就是该用户对此条内容的兴趣得分。根据候选推送内容兴趣得分的高低可以确定给该用户推荐内容的先后顺序，由此可见，本实施例的技术方案中根据用户对推送内容的实际点击行为，修正了兴趣权值，有利于再次更加准确地对用户进行内容推送，最终本实施例结合前述实施例得到的技术方案，其工作流程如图3所示。
需要说明的是，上述各个公式并不是实现本发明的唯一公式，仅作为实施例的一种实现方式。技术人员可以根据业务需要对公式做适当变形，依然落在本发明的范围之内，例如增添参数或倍数值等。
如图4所示，本发明的另一实施还提供了一种基于社交网络的内容推荐系统，其包括：
第一特征提取模块410，用于提取社交网络数据的特征。本实施例中对于社交网络数据的类型不做限定，例如，可以是用户所使用的社交网站、社交工具，例如微博、博客等，例如，可以是社交网站、社交工具的名称、分类、标签内容等。
兴趣权值计算模块420，用于根据某一类型的用户对社交网络数据的行为，计算并记录社交网络数据的特征对于上述类型的用户的兴趣权值。例如，用户在社交网络上频繁发出体育类消息，由此可见用户对体育类内容具有较高的兴趣。
第二特征提取模块430，用于提取多个待推送内容的特征。本实施例中的待推送内容包括但不限于新闻以及资讯，或其他形式的信息。
兴趣得分计算模块440，用于从已记录的特征及兴趣权值中，查找多个待推送内容的特征的兴趣权值，并计算出多个待推送内容对于上述类型的用户的兴趣得分。在本实施例的技术方案中，依据前述的社交网络数据的特征以及相应的兴趣权值可以建立用户的兴趣模型，通过兴趣模型可以选择出需要推送给用户的候选推送内容。
内容待推荐模块450，用于根据多个待推送内容对于上述类型的用户的兴趣得分的高低，对上述类型的用户进行内容推送。本实施例中，基于兴趣得分对待推送内容进行排序，根据排序结果可以确定最终要推荐给用户的内容集合以及顺序。
在本实施例的技术方案中，基于兴趣得分的高低，也即不同类型用户对于待推送内容的兴趣高低进行内容推送，大大减少了人工编辑的工作量，对用户而言，提升了推荐内容的可读性，减少了大量用户不喜欢的推荐内容，节约了用户的时间，推荐质量的提高也会带动更多的用户，提高了每条推荐内容的点击率，最终带来推送流量的稳步提升。
如图5所示，本发明的另一实施例还提供了一种基于社交网络的内容推荐系统，其中，还包括：
第一重新确定模块460，用于根据上述类型的用户对多个待推送内容的点击行为，重新确定多个待推送内容的兴趣得分。
第二重新确定模块470，用于按重新确定的兴趣得分，计算多个待推送内容的特征的兴趣权值并进行记录。
在本实施例的技术方案中，如果用户点击阅读了推送内容，说明推送准确；但如用户对推送的某条内容点击了不感兴趣的按钮，表示用户对于该内容所对应的分类或主题等特征具有较低兴趣，此时根据用户的实际行为估算该内容的兴趣得分，并反向修正该内容中的特征的兴趣权值，以便于在以后使得计算的兴趣得分与用户的实际兴趣更符合。
本发明的另一实施例还提供了一种基于社交网络的内容推荐系统，其中，社交网络数据包括社交网络账号，社交网络数据的特征包括社交网络账号的类别和主题，上述类型的用户对社交网络数据的行为包括对相同类别或相同主题的社交网络账号的关注行为。
在本实施例的技术方案中，以目前流行的微博为例，如用户关注某一媒体账号或者名人账号，则表示用户对该类型或主题的微博账号感兴趣，用户对同一标签下 的微博账号关注的越多，则可设置较高的兴趣权值。目前微博账号均可设置对应的类别、主题或是其他形式的标签；也可以预先为不同微博账号制定至少一个标签，并在标签中记录微博账号的特征，微博账号的标签可以存储在数据库中，以在需要时进行提取。
本发明的另一实施例还提供了一种基于社交网络的内容推荐系统，其中，社交网络数据包括社交网络账号发布的社交内容，社交网络数据的特征包括社交内容的类别和主题，上述类型的用户对社交网络数据的行为包括对相同类别或相同主题的社交内容的转发行为。
在本实施例的技术方案中，以目前流行的微博发出的正文为例，如用户对某一类别或主题的微博账号的正文的转发次数越多，则表示用户对该类别或主题的微博账号的正文具有较高的兴趣，则可以设置较高的兴趣权值。
本发明的另一实施例还提供了一种基于社交网络的内容推荐系统，其中，社交网络数据包括社交网络账号发布的URL，社交网络数据的特征包括URL指向的推送内容的类别和主题，上述类型的用户对社交网络数据的行为包括对相同类别或相同主题的推送内容的URL的点击行为，或对相同类别或相同主题的推送内容上的页面标签的点击行为。
在本实施例的技术方案中，可以预先为不同推送内容设置类别标签，例如，如果推送内容为体育资讯，则其标签设置为体育标签。域名的类别标签可预先存储在数据库中。在本实施例的技术方案中，用户点击了某社交账号发布的URL指向的新闻，则表示用户对于该域名的类别和主题感兴趣，则可以设置较高的兴趣权值。
本发明的另一实施例还提供了一种基于社交网络的内容推荐系统，其中，社交网络数据包括社交网络账号发布的URL，社交网络数据的特征包括URL中包含的域名的类别，上述类型的用户对社交网络数据的行为包括对相同类别的域名对应的URL的点击行为。
在本实施例的技术方案中，可以预先为不同域名设置类别标签，例如，一个域名的类别标签通常是这个域名下的网页所包含的网页的信息类别，比如sports.abc.com，其下的网页可能包含了各个方面的体育信息，则可以把此域名的类别标签确定为“体育”。域名的类别标签可预先存储在数据库中。
在本实施例的技术方案中，用户点击了某社交账号发布的URL指向的新闻，则表示用户对于该域名的类别和主题感兴趣，则可以设置较高的兴趣权值。
本发明的另一实施例还提供了一种基于社交网络的内容推荐系统，其中，第i个待推送内容的兴趣得分为：
Figure PCTCN2015082282-appb-000006
其中，Vi＝x1×w1+x2×w2+…+xN×wN，其中，w1……wN为第i个待推送内容的N个特征，x1……xN为对应N个特征的兴趣权值，a为第一常数，b为第二常数，e、g均为固定常数。
在本实施例的技术方案中，基于上述的得分公式，可以实现一排序模型，该模 型利用上述公式计算兴趣得分。排序模型实际上是一个逻辑回归分类器，该逻辑回归分类器的输入是一条推送内容的特征，输出是一条推送内容针对某一类型的用户的兴趣得分，得分越高表示该类型用户对这条推送内容可能越感兴趣。每条推送内容可以抽象为一个特征向量，向量的每个维度表示该条推送内容的主题、分类，甚至关键词、热度等多个特征。
假设我们已经根据上述的兴趣权值得到模型系数向量为X＝{x1,x2,…,xN}，则可将用来进行推送内容兴趣值计算的逻辑回归分类器表示为：
Figure PCTCN2015082282-appb-000007
其中，V＝XW，X表示上述类型的用户对应的模型系数向量，W表示推送内容的特征向量，上述等式的左边的意义是当向用户推荐一条推送内容newsi时，用户点击的可能性，所以计算得到的右边的兴趣得分可以作为对上述类型用户推送内容的依据。
结合前述的实施例，在用户对推送内容进行处理的情况下，W已知，X未知，求X。
根据用户的点击行为的反馈，可以得到用户点击过的推送内容集合和一批向用户推送过但是用户没有点击的内容集合，对于用户点击过的推送内容newsc，可以得到：
Figure PCTCN2015082282-appb-000008
对于用户没有点击过的推送内容newsd，可以得到：
Figure PCTCN2015082282-appb-000009
这样根据一个用户对m条推送内容点击记录，我们就得到了m个形式如上所述两个表达式的式子，联立求解，即可得到该用户的排序模型系数向量X，也即修正了兴趣权值。
在兴趣权值修正之后，设模型系数向量为{x1,x2,…,xN}，将候选的推送内容集合中的每一条推送内容提取得到对应的特征向量Wi＝{w1,w2,…,wN}，带入到模型中：
Figure PCTCN2015082282-appb-000010
其中，Vi＝x1×w1+x2×w2+…+xN×wN，计算则可得到P(Y＝1|newsi)。这个值就是该用户对此条内容的兴趣得分。根据候选推送内容兴趣得分的高低可以确定给该用户推荐内容的先后顺序，由此可见，本实施例的技术方案中根据用户对推送内容的实际点击行为，修正了兴趣权值，有利于再次更加准确地对用户进行内容推送，最终本实施例结合前述实施例得到的技术方案，其工作流程如图3所示。
根据本发明的实施例，下面将介绍本发明实施例提供的新闻推荐方法和系统。
如图6所示，本发明的一个实施例提供了一种新闻推荐方法，其包括：
步骤610，提取搜索查询数据的特征。本实施例中对于搜索查询数据的类型不做限定，例如，可以是用户对搜索得到的新闻的浏览情况等；本实施例对搜索查询数据的特征也不进行限定，例如，可以是用户浏览的新闻的分类、标题、关键词、新闻来源、网站来源、地域标签、点击率等。
步骤620，根据某一类型用户对搜索查询数据的行为，计算并记录搜索查询数据的特征对于上述类型的用户的兴趣权值。例如，对于浏览行为而言，用户对首先浏览、重复浏览的新闻必然兴趣较高，由此可分析用户的兴趣权值。
步骤630，提取多个待推送新闻的特征。
步骤640，从已记录的特征及兴趣权值中，查找多个待推送新闻的特征的兴趣权值，并计算出多个待推送新闻对于上述类型的用户的兴趣得分。在本实施例的技术方案中，依据前述的搜索查询数据的特征以及相应的兴趣权值可以建立用户的兴趣模型，通过兴趣模型可以选择出需要推送给用户的候选新闻。
步骤650，根据多个待推送新闻对于上述类型的用户的兴趣得分的高低，对用户进行推送。本实施例中，基于兴趣得分对待推送新闻进行排序，根据排序结果可以确定最终要推荐给用户的新闻集合以及顺序。
在本实施例的技术方案中，基于兴趣得分的高低，也即不同类型用户对于待推送新闻的兴趣高低进行新闻推送，大大减少了人工编辑的工作量，对用户而言，提升了新闻的可读性，减少了大量用户不喜欢的新闻，节约了用户的时间，推荐质量的提高也会带动更多的用户，提高了每条新闻的点击率，最终带来新闻流量的稳步提升。
如图7所示，本发明的另一个实施例还提供了一种新闻推荐方法，还包括：
步骤660，根据上述类型的用户对多个待推送新闻的点击行为，重新确定多个待推送新闻的兴趣得分。
步骤670，按重新确定的兴趣得分，计算多个待推送新闻的特征的兴趣权值并进行记录。
在本实施例的技术方案中，用户点击并阅读了推送新闻的话，则说明推送准确；但如用户对推送的某条新闻点击了不感兴趣的按钮，表示用户对于该新闻所对应的分类或主题等特征具有较低兴趣，此时根据用户的实际行为估算该新闻的兴趣得分，并反向修正该新闻的特征的兴趣权值，以便于在以后使得计算的兴趣得分与用户的实际兴趣更符合。
本发明的另一个实施例还提供了一种新闻推荐方法，其中，搜索查询数据包括查询词，搜索查询数据的特征包括查询词的类别和主题，上述类型的用户对搜索查询数据的行为包括对相同类别或相同主题的查询词的查询行为。
在本实施例的技术方案中，可以预先根据查询词对应的新闻集合中新闻的类别标签和主题标签来确定该查询词的类别标签和主题标签，并建立数据库进行存储，则查询词的类别和主题可以从数据库中的类别标签和主题标签中进行提取。比如搜索查询词abc，获取到新闻中最多的主题标签是t1，则该查询词对应的主题标签是t1，获取到新闻最多的分类标签是c1，则该查询词对应的类别标签是c1，则可以提取t1 和c1作为查询词的类别和主题的特征。
本实施例的技术方案中，用户对查询词查询行为的不同主要包括：搜索频次的不同以及搜索时间的不同。搜索某一查询词的频次越高，说明用户的兴趣越高，则可为查询词的类别和主题设置较高的兴趣权值；同时，用户每次搜索该查询词的时间与当前时间越接近，也说明用户的兴趣越高，则可为查询词的类别和主题设置较高的兴趣权值。
本发明的另一个实施例还提供了一种新闻推荐方法，其中，搜索查询数据包括查询结果页上的URL，搜索查询数据的特征包括URL指向的新闻的类别和主题，上述类型的用户对搜索查询数据的行为包括对相同类别或相同主题的新闻的URL的点击行为，或对相同类别或相同主题的新闻上的页面标签的点击行为。
在本实施例的技术方案中，可以预先为每个新闻设置一个类别标签和至少一个主题标签，并在其中记载该新闻的一个类别和至少一个主题。
在本实施例的技术方案中，用户点击阅读了搜索到的某条URL指向的新闻，则表示用户对于该新闻的类别和主题感兴趣，则可以设置较高的兴趣权值；或者，用户点击了某个URL指向的新闻分类频道，且该分类频道的新闻具有同一类别标签，则表示用户对该新闻的类别感兴趣，则可以设置较高的兴趣权值。
本发明的另一个实施例还提供一种新闻推荐方法，其中，搜索查询数据包括社交网络账号发布的URL，搜索查询数据的特征包括URL中包含的域名的类别，上述类型的用户对搜索查询数据的行为包括对相同类别的域名对应的URL的点击行为。
在本实施例的技术方案中，可以预先为不同域名设置类别标签，例如，一个域名的类别标签通常是这个域名下的网页所包含的网页的信息类别，比如sports.abc.com，其下的网页可能包含了各个方面的体育信息，则可以把此域名的类别标签确定为“体育”。域名的类别标签可预先存储在数据库中。
在本实施例的技术方案中，用户搜索到某社交账号发布的URL，并点击阅读该URL指向的新闻，则表示用户对于该域名的类别和主题感兴趣，则可以设置较高的兴趣权值。
本发明的另一个实施例还提供了一种新闻推荐方法，其中，第i个待推送新闻的兴趣得分为：
Figure PCTCN2015082282-appb-000011
其中，Vi＝x1×w1+x2×w2+…+xN×wN，其中，w1……wN为第i个待推送新闻的N个特征，x1……xN为对应N个特征的兴趣权值，a为第一常数，b为第二常数，e、g均为固定常数。
在本实施例的技术方案中，基于上述的得分公式，可以实现一排序模型，该模型利用上述公式计算兴趣得分。排序模型实际上是一个逻辑回归分类器，该逻辑回归分类器的输入是一条新闻的特征，输出是一条新闻针对某一类型的用户的兴趣得分，得分越高表示该类型用户对这条新闻可能越感兴趣。每条新闻可以抽象为一个 特征向量，向量的每个维度表示该条新闻的主题、分类，甚至关键词、热度等多个特征。
假设我们已经根据上述的兴趣权值得到模型系数向量为X＝{x1,x2,…,xN}，则可将用来进行新闻兴趣值计算的逻辑回归分类器表示为：
Figure PCTCN2015082282-appb-000012
其中，V＝XW，X表示上述类型的用户对应的模型系数向量，W表示新闻的特征向量，上述等式的左边的意义是当向用户推荐一条新闻newsi时，用户点击的可能性，所以计算得到的右边的兴趣得分可以作为对上述类型用户推送新闻的依据。
结合前述的实施例，在用户对推送新闻进行处理的情况下，W已知，X未知，求X。
根据用户的点击行为的反馈，可以得到用户点击过的新闻集合和一批向用户推送过但是用户没有点击的新闻集合，对于用户点击过的新闻newsc，可以得到：
Figure PCTCN2015082282-appb-000013
对于用户没有点击过的新闻newsd，可以得到：
Figure PCTCN2015082282-appb-000014
这样根据一个用户对m条推送新闻点击记录，我们就得到了m个形式如上所述两个表达式的式子，联立求解，即可得到该用户的排序模型系数向量X，也即修正了兴趣权值。
在兴趣权值修正之后，设模型系数向量为{x1,x2,…,xN}，将候选的新闻集合中的每一条新闻提取得到对应的特征向量Wi＝{w1,w2,…,wN}，带入到模型中：
Figure PCTCN2015082282-appb-000015
其中，Vi＝x1×w1+x2×w2+…+xN×wN，计算则可得到P(Y＝1|newsi)。这个值就是该用户对此条新闻的兴趣得分。根据候选新闻兴趣得分的高低可以确定给该用户推荐新闻的先后顺序，由此可见，本实施例的技术方案中根据用户对推送新闻的实际点击行为，修正了兴趣权值，有利于再次更加准确地对用户进行新闻推送，最终本实施例结合前述实施例得到的技术方案，其工作流程如图8所示。
需要说明的是，上述各个公式并不是实现本发明的唯一公式，仅作为实施例的一种实现方式。技术人员可以根据业务需要对公式做适当变形，依然落在本发明的 范围之内，例如增添参数或倍数值等。
如图9所示，本发明的另一实施例还提供了一种新闻推荐系统，其包括：
第一特征提取模块910，用于提取搜索查询数据的特征。本实施例中对于搜索查询数据的类型不做限定，例如，可以是用户对搜索得到的新闻的浏览情况等；本实施例对搜索查询数据的特征也不进行限定，例如，可以是用户浏览的新闻的分类、标题、关键词、新闻来源、网站来源、地域标签、点击率等。
兴趣权值计算模块920，用于根据用户对搜索查询数据的行为，计算并记录搜索查询数据的特征对于上述类型的用户的兴趣权值。例如，对于浏览行为而言，用户对首先浏览、重复浏览的新闻必然兴趣较高，由此可分析用户的兴趣权值。
第二特征提取模块930，用于提取多个待推送新闻的特征。
兴趣得分计算模块940，用于从已记录的特征及兴趣权值中，查找多个待推送新闻的特征的兴趣权值，并计算出多个待推送新闻对于上述类型的用户的兴趣得分。在本实施例的技术方案中，依据前述的搜索查询数据的特征以及相应的兴趣权值可以建立用户的兴趣模型，通过兴趣模型可以选择出需要推送给用户的候选新闻。
待推送新闻推荐模块950，用于根据多个待推送新闻对于上述类型的用户的兴趣得分的高低，按顺序将多个待推送新闻推送给上述类型的用户。本实施例中，基于兴趣得分对待推送新闻进行排序，根据排序结果可以确定最终要推荐给用户的新闻集合以及顺序。
在本实施例的技术方案中，基于兴趣得分的高低，也即不同类型用户对于待推送新闻的兴趣高低进行新闻推送，大大减少了人工编辑的工作量，对用户而言，提升了新闻的可读性，减少了大量用户不喜欢的新闻，节约了用户的时间，推荐质量的提高也会带动更多的用户，提高了每条新闻的点击率，最终带来新闻流量的稳步提升。
如图10所示，本发明的另一个实施例还提供了一种新闻推荐系统，还包括：
第一重新确定模块960，用于根据上述类型的用户对多个待推送新闻的点击行为，重新确定多个待推送新闻的兴趣得分。
第二重新确定模块970，用于按重新确定的兴趣得分，计算多个待推送新闻的特征的兴趣权值并进行记录。
本实施例的技术方案中，用户点击并阅读了推送新闻的话，则说明推送准确；但如用户对推送的某条新闻点击了不感兴趣的按钮，表示用户对于该新闻所对应的分类或主题等特征具有较低兴趣，此时根据用户的实际行为估算该新闻的兴趣得分，并反向修正该新闻的特征的兴趣权值，以便于在以后使得计算的兴趣得分与用户的实际兴趣更符合。
本发明的另一个实施例还提供了一种新闻推荐系统，其中，搜索查询数据包括查询词，搜索查询数据的特征包括查询词的类别和主题，上述类型的用户对搜索查询数据的行为包括对相同类别或相同主题的查询词的查询行为。
在本实施例的技术方案中，可以预先根据查询词对应的新闻集合中新闻的类别标签和主题标签来确定该查询词的类别标签和主题标签，并建立数据库进行存储，则查询词的类别和主题可以从数据库中的类别标签和主题标签中进行提取。比如搜 索查询词abc，获取到新闻中最多的主题标签是t1，则该查询词对应的主题标签是t1，获取到新闻最多的分类标签是c1，则该查询词对应的类别标签是c1，则可以提取t1和c1作为查询词的类别和主题的特征。
本实施例的技术方案中，用户对查询词查询行为的不同主要包括：搜索频次的不同以及搜索时间的不同。搜索某一查询词的频次越高，说明用户的兴趣越高，则可为查询词的类别和主题设置较高的兴趣权值；同时，用户每次搜索该查询词的时间与当前时间越接近，也说明用户的兴趣越高，则可为查询词的类别和主题设置较高的兴趣权值。
本发明的另一个实施例还提供了一种新闻推荐系统，其中，搜索查询数据包括查询结果页上的URL，搜索查询数据的特征包括URL指向的新闻的类别，上述类型的用户对搜索查询数据的行为包括对相同类别的新闻的URL的点击行为，或对相同类别或相同主题的新闻上的页面标签的点击行为。
在本实施例的技术方案中，可以预先为每个新闻设置一个类别标签和至少一个主题标签，并在其中记载该新闻的一个类别和至少一个主题。
在本实施例的技术方案中，用户点击阅读了搜索到的某条URL指向的新闻，则表示用户对于该新闻的类别和主题感兴趣，则可以设置较高的兴趣权值；或者，用户点击了某个URL指向的新闻分类频道，且该分类频道的新闻具有同一类别标签，则表示用户对该新闻的类别感兴趣，则可以设置较高的兴趣权值。
本发明的另一个实施例还提供了一种新闻推荐系统，其中，搜索查询数据包括社交网络账号发布的URL，搜索查询数据的特征包括URL中包含的域名的类别和主题，上述类型的用户对搜索查询数据的行为包括对相同类别或相同主题的域名对应的URL的点击行为。
在本实施例的技术方案中，可以预先为不同域名设置类别标签，例如，一个域名的类别标签通常是这个域名下的网页所包含的网页的信息类别，比如sports.abc.com，其下的网页可能包含了各个方面的体育信息，则可以把此域名的类别标签确定为“体育”。域名的类别标签可预先存储在数据库中。
在本实施例的技术方案中，用户搜索到某社交账号发布的URL，并点击阅读该URL指向的新闻，则表示用户对于该域名的类别和主题感兴趣，则可以设置较高的兴趣权值。
本发明的另一个实施例还提供了一种新闻推荐系统，其中，第i个待推送新闻的兴趣得分为：
Figure PCTCN2015082282-appb-000016
其中，Vi＝x1×w1+x2×w2+…+xN×wN，其中，w1……wN为第i个待推送新闻的N个特征，x1……xN为对应N个特征的兴趣权值，a为第一常数，b为第二常数，e、g均为固定常数。
在本实施例的技术方案中，基于上述的得分公式，可以实现一排序模型，该模型利 用上述公式计算兴趣得分。排序模型实际上是一个逻辑回归分类器，该逻辑回归分类器的输入是一条新闻的特征，输出是一条新闻针对某一类型的用户的兴趣得分，得分越高表示该类型用户对这条新闻可能越感兴趣。每条新闻可以抽象为一个特征向量，向量的每个维度表示该条新闻的主题、分类，甚至关键词、热度等多个特征。
假设我们已经根据上述的兴趣权值得到模型系数向量为X＝{x1,x2,…,xN}，则可将用来进行新闻兴趣值计算的逻辑回归分类器表示为：
Figure PCTCN2015082282-appb-000017
其中，V＝XW，X表示上述类型的用户对应的模型系数向量，W表示新闻的特征向量，上述等式的左边的意义是当向用户推荐一条新闻newsi时，用户点击的可能性，所以计算得到的右边的兴趣得分可以作为对上述类型用户推送新闻的依据。
结合前述的实施例，在用户对推送新闻进行处理的情况下，W已知，X未知，求X。
根据用户的点击行为的反馈，可以得到用户点击过的新闻集合和一批向用户推送过但是用户没有点击的新闻集合，对于用户点击过的新闻newsc，可以得到：
Figure PCTCN2015082282-appb-000018
对于用户没有点击过的新闻newsd，可以得到：
Figure PCTCN2015082282-appb-000019
这样根据一个用户对m条推送新闻点击记录，我们就得到了m个形式如上所述两个表达式的式子，联立求解，即可得到该用户的排序模型系数向量X，也即修正了兴趣权值。
在兴趣权值修正之后，设模型系数向量为{x1,x2,…,xN}，将候选的新闻集合中的每一条新闻提取得到对应的特征向量Wi＝{w1,w2,…,wN}，带入到模型中：
Figure PCTCN2015082282-appb-000020
其中，Vi＝x1×w1+x2×w2+…+xN×wN，计算则可得到P(Y＝1|newsi)。这个值就是该用户对此条新闻的兴趣得分。根据候选新闻兴趣得分的高低可以确定给该用户推荐新闻的先后顺序，由此可见，本实施例的技术方案中根据用户对推送新闻的实际点击行为，修正了兴趣权值，有利于再次更加准确地对用户进行新闻推送，最终本实施例结合前述实施例得到的技术方案，其工作流程如图8所示。
在此处所提供的说明书中，说明了大量具体细节。然而，能够理解，本发明的实施例可以在没有这些具体细节的情况下实践。在一些实例中，并未详细示出公知 的方法、结构和技术，以便不模糊对本说明书的理解。
类似地，应当理解，为了精简本公开并帮助理解各个发明方面中的一个或多个，在上面对本发明的示例性实施例的描述中，本发明的各个特征有时被一起分组到单个实施例、图、或者对其的描述中。然而，并不应将该公开的方法解释成反映如下意图：即所要求保护的本发明要求比在每个权利要求中所明确记载的特征更多的特征。更确切地说，如下面的权利要求书所反映的那样，发明方面在于少于前面公开的单个实施例的所有特征。因此，遵循具体实施方式的权利要求书由此明确地并入该具体实施方式，其中每个权利要求本身都作为本发明的单独实施例。
本领域那些技术人员可以理解，可以对实施例中的设备中的模块进行自适应性地改变并且把它们设置在与该实施例不同的一个或多个设备中。可以把实施例中的模块或单元或组件组合成一个模块或单元或组件，以及此外可以把它们分成多个子模块或子单元或子组件。除了这样的特征和/或过程或者单元中的至少一些是相互排斥之外，可以采用任何组合对本说明书(包括伴随的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者设备的所有过程或单元进行组合。除非另外明确陈述，本说明书(包括伴随的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
此外，本领域的技术人员能够理解，尽管在此所述的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本发明的范围之内并且形成不同的实施例。例如，在下面的权利要求书中，所要求保护的实施例的任意之一都可以以任意的组合方式来使用。
本发明的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件模块实现，或者以它们的组合实现。本领域的技术人员应当理解，可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本发明实施例的基于社交网络的内容推荐系统，以及新闻推荐系统中的一些或者全部部件的一些或者全部功能。本发明还可以实现为用于执行这里所描述的方法的一部分或者全部的设备或者装置程序(例如，计算机程序和计算机程序产品)。这样的实现本发明的程序可以存储在计算机可读介质上，或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载得到，或者在载体信号上提供，或者以任何其他形式提供。
例如，图11示出了可以实现在智能终端之间传输数据的方法的计算设备。该计算设备传统上包括处理器1110和以存储器1120形式的计算机程序产品或者计算机可读介质。存储器1120可以是诸如闪存、EEPROM(电可擦除可编程只读存储器)、EPROM、硬盘或者ROM之类的电子存储器。存储器1120具有用于执行上述方法中的任何方法步骤的程序代码1131的存储空间1130。例如，用于程序代码的存储空间1130可以包括分别用于实现上面的方法中的各种步骤的各个程序代码1131。这些程序代码可以从一个或者多个计算机程序产品中读出或者写入到这一个或者多个计算机程序产品中。这些计算机程序产品包括诸如硬盘，紧致盘(CD)、存储卡或者软盘之类的程序代码载体。这样的计算机程序产品通常为如参考图12所述的便携式或者固定存储单元。该存储单元可以具有与图11的计算设备中的存储器1120类似布置的存储段、存储空间等。程序代码可以例如以适当形式进行压缩。通常，存储单 元包括计算机可读代码1131’，即可以由例如诸如1110之类的处理器读取的代码，这些代码当由计算设备运行时，导致该计算设备执行上面所描述的方法中的各个步骤。
本文中所称的“一个实施例”、“实施例”或者“一个或者多个实施例”意味着，结合实施例描述的特定特征、结构或者特性包括在本发明的至少一个实施例中。此外，请注意，这里“在一个实施例中”的词语例子不一定全指同一个实施例。
应该注意的是上述实施例对本发明进行说明而不是对本发明进行限制，并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中，不应将位于括号之间的任何参考符号构造成对权利要求的限制。单词“包含”不排除存在未列在权利要求中的元件或步骤。位于元件之前的单词“一”或“一个”不排除存在多个这样的元件。本发明可以借助于包括有若干不同元件的硬件以及借助于适当编程的计算机来实现。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。单词第一、第二、以及第三等的使用不表示任何顺序。可将这些单词解释为名称。
此外，还应当注意，本说明书中使用的语言主要是为了可读性和教导的目的而选择的，而不是为了解释或者限定本发明的主题而选择的。因此，在不偏离所附权利要求书的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。对于本发明的范围，对本发明所做的公开是说明性的，而非限制性的，本发明的范围由所附权利要求书限定。

Claims (22)

  1. 一种基于社交网络的内容推荐方法，其包括：
    提取社交网络数据的特征；
    根据某一类型的用户对所述社交网络数据的行为，计算并记录所述社交网络数据的特征对于所述类型的用户的兴趣权值；
    提取多个待推送内容的特征；
    从已记录的特征及兴趣权值中，查找所述多个待推送内容的特征的兴趣权值，并计算出所述多个待推送内容对于所述类型的用户的兴趣得分；
    根据所述多个待推送内容对于所述类型的用户的兴趣得分的高低，对所述类型的用户进行内容推送。
  2. 根据权利要求1所述的基于社交网络的内容推荐方法，其中，还包括：
    根据所述类型的用户对所述多个待推送内容的点击行为，重新确定所述多个待推送内容的兴趣得分；
    按重新确定的兴趣得分，计算所述多个待推送内容的特征的兴趣权值并进行记录。
  3. 根据权利要求1-2任一项所述的基于社交网络的内容推荐方法，其中，所述社交网络数据包括社交网络账号，所述社交网络数据的特征包括所述社交网络账号的类别和主题，所述类型的用户对所述社交网络数据的行为包括对相同类别或相同主题的所述社交网络账号的关注行为。
  4. 根据权利要求1-3任一项所述的基于社交网络的内容推荐方法，其中，所述社交网络数据包括社交网络账号发布的社交内容，所述社交网络数据的特征包括所述社交内容的类别和主题，所述类型的用户对所述社交网络数据的行为包括对相同类别或相同主题的所述社交内容的转发行为。
  5. 根据权利要求1-4任一项所述的基于社交网络的内容推荐方法，其中，所述社交网络数据包括社交网络账号发布的URL，所述社交网络数据的特征包括所述URL指向的推送内容的类别和主题，所述类型的用户对所述社交网络数据的行为包括对相同类别或相同主题的推送内容的URL的点击行为，或对相同类别或相同主题的推送内容上的页面标签的点击行为。
  6. 根据权利要求1-5任一项所述的基于社交网络的内容推荐方法，其中，所述社交网络数据包括社交网络账号发布的URL，所述社交网络数据的特征包括所述URL中包含的域名的类别，所述类型的用户对所述社交网络数据的行为包括对相同类别的域名对应的URL的点击行为。
  7. 一种新闻推荐方法，其包括：
    提取搜索查询数据的特征；
    根据某一类型用户对所述搜索查询数据的行为，计算并记录所述搜索查询数据的特征对于所述类型用户的兴趣权值；
    提取多个待推送新闻的特征；
    从已记录的特征及兴趣权值中，查找所述多个待推送新闻的特征的兴趣权值，并计算出所述多个待推送新闻对于所述类型的用户的兴趣得分；
    根据所述多个待推送新闻对于所述类型的用户的兴趣得分的高低，对所述类型的用户进行新闻推送。
  8. 根据权利要求7所述的新闻推荐方法，其中，还包括：
    根据所述类型的用户对所述多个待推送新闻的点击行为，重新确定所述多个待推送新闻的兴趣得分；
    按重新确定的兴趣得分，计算所述多个待推送新闻的特征的兴趣权值并进行记录。
  9. 根据权利要求7-8任一项所述的新闻推荐方法，其中，所述搜索查询数据包括查询词，所述搜索查询数据的特征包括所述查询词的类别和主题，所述类型的用户对所述搜索查询数据的行为包括对相同类别或相同主题的所述查询词的查询行为。
  10. 根据权利要求7-9任一项所述的新闻推荐方法，其中，所述搜索查询数据包括查询结果页上的URL，所述搜索查询数据的特征包括所述URL指向的新闻的类别和主题，所述类型的用户对所述搜索查询数据的行为包括对相同类别或相同主题的新闻的URL的点击行为，或对相同类别或相同主题的新闻上的页面标签的点击行为。
  11. 根据权利要求7-10任一项所述的新闻推荐方法，其中，所述搜索查询数据包括社交网络账号发布的URL，所述搜索查询数据的特征包括所述URL中包含的域名的类别，所述类型的用户对所述搜索查询数据的行为包括对相同类别的域名对应的URL的点击行为。
  12. 一种基于社交网络的内容推荐系统，其包括：
    第一特征提取模块，用于提取社交网络数据的特征；
    兴趣权值计算模块，用于根据某一类型的用户对所述社交网络数据的行为，计算并记录所述社交网络数据的特征对于所述类型的用户的兴趣权值；
    第二特征提取模块，用于提取多个待推送内容的特征；
    兴趣得分计算模块，用于从已记录的特征及兴趣权值中，查找所述多个待推送内容的特征的兴趣权值，并计算出所述多个待推送内容对于所述类型的用户的兴趣得分；
    内容待推荐模块，用于根据所述多个待推送内容对于所述类型的用户的兴趣得分的高低，对所述类型的用户进行内容推送。
  13. 根据权利要求12所述的基于社交网络的内容推荐系统，其中，还包括：
    第一重新确定模块，用于根据所述类型的用户对所述多个待推送内容的点击行为，重新确定所述多个待推送内容的兴趣得分；
    第二重新确定模块，用于按重新确定的兴趣得分，计算所述多个待推送内容的特征的兴趣权值并进行记录。
  14. 根据权利要求12-13任一项所述的基于社交网络的内容推荐系统，其中，所述社交网络数据包括社交网络账号，所述社交网络数据的特征包括所述社交网络 账号的类别和主题，所述类型的用户对所述社交网络数据的行为包括对相同类别或相同主题的所述社交网络账号的关注行为。
  15. 根据权利要求12-14任一项所述的基于社交网络的内容推荐系统，其中，所述社交网络数据包括社交网络账号发布的社交内容，所述社交网络数据的特征包括所述社交内容的类别和主题，所述类型的用户对所述社交网络数据的行为包括对相同类别或相同主题的所述社交内容的转发行为。
  16. 一种新闻推荐系统，其包括：
    第一特征提取模块，用于提取搜索查询数据的特征；
    兴趣权值计算模块，用于根据用户对所述搜索查询数据的行为，计算并记录所述搜索查询数据的特征对于所述类型的用户的兴趣权值；
    第二特征提取模块，用于提取多个待推送新闻的特征；
    兴趣得分计算模块，用于从已记录的特征及兴趣权值中，查找所述多个待推送新闻的特征的兴趣权值，并计算出所述多个待推送新闻对于所述类型的用户的兴趣得分；
    待推送新闻推荐模块，用于根据所述多个待推送新闻对于所述类型的用户的兴趣得分的高低，对所述类型的用户进行新闻推送。
  17. 根据权利要求16所述的新闻推荐系统，其中，还包括：
    第一重新确定模块，用于根据所述类型的用户对所述多个待推送新闻的点击行为，重新确定所述多个待推送新闻的兴趣得分；
    第二重新确定模块，用于按重新确定的兴趣得分，计算所述多个待推送新闻的特征的兴趣权值并进行记录。
  18. 根据权利要求16-17任一项所述的新闻推荐系统，其中，所述搜索查询数据包括查询词，所述搜索查询数据的特征包括所述查询词的类别和主题，所述类型的用户对所述搜索查询数据的行为包括对相同类别或相同主题的所述查询词的查询行为。
  19. 根据权利要求16-18任一项所述的新闻推荐系统，其中，所述搜索查询数据包括查询结果页上的URL，所述搜索查询数据的特征包括所述URL指向的新闻的类别和主题，所述类型的用户对所述搜索查询数据的行为包括对相同类别或相同主题的新闻的URL的点击行为，或对相同类别或相同主题的新闻上的页面标签的点击行为。
  20. 根据权利要求16-19任一项所述的新闻推荐系统，其中，所述搜索查询数据包括社交网络账号发布的URL，所述搜索查询数据的特征包括所述URL中包含的域名的类别，所述类型的用户对所述搜索查询数据的行为包括对相同类别的域名对应的URL的点击行为。
  21. 一种计算机程序，包括计算机可读代码，当所述计算机可读代码在计算设备上运行时，导致所述计算设备执行根据权利要求1-11任一项所述的方法。
  22. 一种计算机可读介质，其中存储了如权利要求21所述的计算机程序。
PCT/CN2015/082282 2014-06-30 2015-06-25 基于社交网络的内容、新闻推荐方法和系统 Ceased WO2016000555A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US15/323,306 US20170154116A1 (en) 2014-06-30 2015-06-25 Method and system for recommending contents based on social network

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
CN201410307116.X 2014-06-30
CN201410308039.X 2014-06-30
CN201410308039.XA CN104063476A (zh) 2014-06-30 2014-06-30 基于社交网络的内容推荐方法和系统
CN201410307116.XA CN104036038A (zh) 2014-06-30 2014-06-30 新闻推荐方法和系统

Publications (1)

Publication Number Publication Date
WO2016000555A1 true WO2016000555A1 (zh) 2016-01-07

Family

ID=55018436

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2015/082282 Ceased WO2016000555A1 (zh) 2014-06-30 2015-06-25 基于社交网络的内容、新闻推荐方法和系统

Country Status (2)

Country Link
US (1) US20170154116A1 (zh)
WO (1) WO2016000555A1 (zh)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106951463A (zh) * 2017-02-27 2017-07-14 宇龙计算机通信科技(深圳)有限公司 新闻推送方法及系统
CN110119476A (zh) * 2019-04-26 2019-08-13 广州美术学院 一种账号自动推荐方法、装置、终端设备及存储介质
CN111858688A (zh) * 2020-07-20 2020-10-30 海尔优家智能科技（北京）有限公司 一种纺织材料以及色卡推荐方法、装置及存储介质
CN114201683A (zh) * 2021-12-15 2022-03-18 齐鲁工业大学 一种基于多级匹配的兴趣激活新闻推荐方法及系统
CN114416246A (zh) * 2021-12-31 2022-04-29 北京五八信息技术有限公司 一种数据处理方法、装置、电子设备及存储介质
CN116546091A (zh) * 2023-07-07 2023-08-04 深圳市四格互联信息技术有限公司 流式内容的推荐方法、装置、设备及存储介质
CN117216391A (zh) * 2023-09-18 2023-12-12 联奕科技股份有限公司 一种新闻推荐方法、系统及设备
US11922300B2 (en) 2016-03-01 2024-03-05 Microsoft Technology Licensing, Llc. Automated commentary for online content

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10380146B2 (en) * 2015-08-17 2019-08-13 Oath Inc. Locale of interest identification
CN107707655A (zh) * 2017-10-10 2018-02-16 珠海云麦科技有限公司 一种信息推广方法及装置
CN110472021A (zh) * 2018-05-11 2019-11-19 微软技术许可有限责任公司 会话中推荐新闻的技术
CN110737822B (zh) * 2018-07-03 2022-07-26 百度在线网络技术（北京）有限公司 用户兴趣挖掘方法、装置、设备和存储介质
US10394859B1 (en) * 2018-10-19 2019-08-27 Palantir Technologies Inc. Systems and methods for processing and displaying time-related geospatial data
US10805374B1 (en) 2019-08-19 2020-10-13 Palantir Technologies Inc. Systems and methods for providing real-time streaming data processing at edge servers
CN111401820A (zh) * 2020-04-09 2020-07-10 福建好运联联信息科技有限公司 一种提高社交平台的物流效率的方法及终端
CN112053078A (zh) * 2020-09-15 2020-12-08 山东爱城市网信息技术有限公司 政务服务pgc社区的系统及实现方法
CN112328861B (zh) * 2020-11-24 2023-06-23 郑州航空工业管理学院 一种基于大数据处理的新闻传播方法
CN113254764B (zh) * 2021-05-13 2022-05-27 天津大学 基于综合传播影响力增长指数的新闻推荐方法及系统
CN115481236A (zh) * 2022-08-31 2022-12-16 电子科技大学 一种基于用户兴趣建模的新闻推荐方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102611785A (zh) * 2011-01-20 2012-07-25 北京邮电大学 面向手机的移动用户个性化新闻主动推荐服务系统及方法
CN102867016A (zh) * 2012-07-18 2013-01-09 北京开心人信息技术有限公司 一种基于标签的社交网络用户兴趣挖掘方法与装置
CN104036038A (zh) * 2014-06-30 2014-09-10 北京奇虎科技有限公司 新闻推荐方法和系统
CN104063476A (zh) * 2014-06-30 2014-09-24 北京奇虎科技有限公司 基于社交网络的内容推荐方法和系统

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4967472B2 (ja) * 2006-06-22 2012-07-04 富士電機株式会社 半導体装置
US8429702B2 (en) * 2006-09-11 2013-04-23 At&T Intellectual Property I, L.P. Methods and apparatus for selecting and pushing customized electronic media content
KR101427104B1 (ko) * 2008-01-22 2014-08-06 에스케이플래닛 주식회사 소셜 네트워크 기반의 콘텐츠 추천 시스템과 방법 및콘텐츠 제공서버
CN102316046B (zh) * 2010-06-29 2016-03-30 国际商业机器公司 向社交网络中的用户推荐信息的方法和装置
US9213729B2 (en) * 2012-01-04 2015-12-15 Trustgo Mobile, Inc. Application recommendation system
CN103714067B (zh) * 2012-09-29 2018-01-26 腾讯科技（深圳）有限公司 一种信息推送方法和装置
KR20140102381A (ko) * 2013-02-13 2014-08-22 삼성전자주식회사 전자 기기 및 이의 컨텐츠 추천 방법
CN103294800B (zh) * 2013-05-27 2016-12-28 华为技术有限公司 一种信息推送方法及装置
US9554258B2 (en) * 2014-04-03 2017-01-24 Toyota Jidosha Kabushiki Kaisha System for dynamic content recommendation using social network data

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102611785A (zh) * 2011-01-20 2012-07-25 北京邮电大学 面向手机的移动用户个性化新闻主动推荐服务系统及方法
CN102867016A (zh) * 2012-07-18 2013-01-09 北京开心人信息技术有限公司 一种基于标签的社交网络用户兴趣挖掘方法与装置
CN104036038A (zh) * 2014-06-30 2014-09-10 北京奇虎科技有限公司 新闻推荐方法和系统
CN104063476A (zh) * 2014-06-30 2014-09-24 北京奇虎科技有限公司 基于社交网络的内容推荐方法和系统

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11922300B2 (en) 2016-03-01 2024-03-05 Microsoft Technology Licensing, Llc. Automated commentary for online content
CN106951463A (zh) * 2017-02-27 2017-07-14 宇龙计算机通信科技(深圳)有限公司 新闻推送方法及系统
CN110119476A (zh) * 2019-04-26 2019-08-13 广州美术学院 一种账号自动推荐方法、装置、终端设备及存储介质
CN111858688A (zh) * 2020-07-20 2020-10-30 海尔优家智能科技（北京）有限公司 一种纺织材料以及色卡推荐方法、装置及存储介质
CN111858688B (zh) * 2020-07-20 2024-05-24 海尔优家智能科技（北京）有限公司 一种纺织材料以及色卡推荐方法、装置及存储介质
CN114201683A (zh) * 2021-12-15 2022-03-18 齐鲁工业大学 一种基于多级匹配的兴趣激活新闻推荐方法及系统
CN114416246A (zh) * 2021-12-31 2022-04-29 北京五八信息技术有限公司 一种数据处理方法、装置、电子设备及存储介质
CN114416246B (zh) * 2021-12-31 2024-03-19 北京五八信息技术有限公司 一种数据处理方法、装置、电子设备及存储介质
CN116546091A (zh) * 2023-07-07 2023-08-04 深圳市四格互联信息技术有限公司 流式内容的推荐方法、装置、设备及存储介质
CN116546091B (zh) * 2023-07-07 2023-11-28 深圳市四格互联信息技术有限公司 流式内容的推荐方法、装置、设备及存储介质
CN117216391A (zh) * 2023-09-18 2023-12-12 联奕科技股份有限公司 一种新闻推荐方法、系统及设备
CN117216391B (zh) * 2023-09-18 2024-05-07 联奕科技股份有限公司 一种新闻推荐方法、系统及设备

Also Published As

Publication number Publication date
US20170154116A1 (en) 2017-06-01

Similar Documents

Publication Publication Date Title
US20170154116A1 (en) Method and system for recommending contents based on social network
CN104281622B (zh) 一种社交媒体中的信息推荐方法和装置
CN104036038A (zh) 新闻推荐方法和系统
CN104063476A (zh) 基于社交网络的内容推荐方法和系统
CN106649818B (zh) 应用搜索意图的识别方法、装置、应用搜索方法和服务器
US9317613B2 (en) Large scale entity-specific resource classification
US9430568B2 (en) Method and system for querying information
US9928296B2 (en) Search lexicon expansion
CN111104526A (zh) 一种基于关键词语义的金融标签提取方法及系统
CN104102639B (zh) 基于文本分类的推广触发方法和装置
CN107273436A (zh) 一种推荐模型的训练方法和训练装置
JP6056610B2 (ja) テキスト情報処理装置、テキスト情報処理方法、及びテキスト情報処理プログラム
CN103838798B (zh) 页面分类系统及页面分类方法
KR20160057475A (ko) 소셜 데이터를 능동적으로 획득하기 위한 시스템 및 방법
CN103778260A (zh) 一种个性化微博信息推荐系统和方法
US20110307432A1 (en) Relevance for name segment searches
CN104035972B (zh) 一种基于微博的知识推荐方法与系统
Vosecky et al. Searching for quality microblog posts: Filtering and ranking based on content analysis and implicit links
JP5012078B2 (ja) カテゴリ作成方法、カテゴリ作成装置、およびプログラム
CN108846097A (zh) 用户的兴趣标签表示方法、文章推荐方法、及装置、设备
CN113468339B (zh) 基于知识图谱的标签提取方法、系统、电子设备及介质
CN103577534A (zh) 搜索方法和搜索引擎
WO2016101812A1 (zh) 用于对搜索数据进行处理的方法及设备
CN107766398A (zh) 用于使图像与内容项目匹配的方法、装置和数据处理系统
CN107291755A (zh) 一种终端推送方法及装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15815974

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

WWE Wipo information: entry into national phase

Ref document number: 15323306

Country of ref document: US

122 Ep: pct application non-entry in european phase

Ref document number: 15815974

Country of ref document: EP

Kind code of ref document: A1