WO2017121076A1 - Procédé et dispositif de poussée d'informations - Google Patents

Procédé et dispositif de poussée d'informations Download PDF

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Publication number
WO2017121076A1
WO2017121076A1 PCT/CN2016/087453 CN2016087453W WO2017121076A1 WO 2017121076 A1 WO2017121076 A1 WO 2017121076A1 CN 2016087453 W CN2016087453 W CN 2016087453W WO 2017121076 A1 WO2017121076 A1 WO 2017121076A1
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Prior art keywords
information
website
identification
model
identification information
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PCT/CN2016/087453
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English (en)
Chinese (zh)
Inventor
岳爱珍
崔燕
杨自强
谭静
高显
赵辉
王私江
于倩
白霄骅
Original Assignee
百度在线网络技术（北京）有限公司
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Publication of WO2017121076A1 publication Critical patent/WO2017121076A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06F16/951—Indexing; Web crawling techniques

Definitions

  • the present application relates to the field of computer technologies, and in particular, to the field of Internet technologies, and in particular, to an information push method and apparatus.
  • the existing information push mode generally pushes various candidate push information directly to the user without adding an identifier to the information, so that there is no difference in the logo between the pushed information, and the user is less efficient in obtaining the information.
  • the purpose of the present application is to propose an improved information push method and apparatus to solve the technical problems mentioned in the background section above.
  • the present application provides an information pushing method, the method comprising: acquiring candidate push information; determining, according to a pre-trained information identification model, identification information corresponding to the candidate push information; and based on the candidate push information and And the identifier information corresponding to the candidate push information is generated, and the information to be pushed is pushed; and the information to be pushed is pushed.
  • the determining, according to the pre-trained information identification model, the identification information corresponding to the candidate push information comprising: confirming a website from which the candidate push information is derived; searching for feature information of the website, Introducing feature information into pre-trained And the identifier information corresponding to the feature information of the website determined by the information identification model, and the identifier information corresponding to the feature information of the website is used as the identifier information corresponding to the candidate push information.
  • the feature information includes at least one of the following information of the website: server quantity information, domain name age information, ranking information, keyword ranking information, bounce rate information, outer chain number information, and flow information. , weight information, website organizer information.
  • the method further comprises the step of establishing an information identification model, comprising: obtaining sample data required to train the model, wherein the sample data includes feature information of the sample website and the determined sample website The identification information corresponding to the feature information; predicting the identification information corresponding to the feature information of the sample website based on the initial model, and acquiring the identification information corresponding to the feature information of the sample website predicted by the initial model, wherein the initial model is one of the following models : support vector machine model, decision tree model, naive Bayesian model, logistic regression model; determine whether the identification information corresponding to the feature information of the sample website predicted by the initial model is consistent with the identification information corresponding to the determined feature information of the sample website; If not, the feature information of the sample website and the identification information corresponding to the determined feature information of the sample website are used as the training data of the initial model, and the parameters of the initial model are modified based on the training data, Obtaining the information identification model.
  • the identification information includes first identification information and second identification information; and the determining, according to the pre-trained model, the identification information corresponding to the candidate push information, including: based on the searched Whether the preset information is included in the record information of the website, and one of the first identification information and the second identification information is selected as the identification information corresponding to the candidate push information; or, based on the acquired user report information set Whether the information of the website is included, one of the first identification information and the second identification information is selected as the identification information corresponding to the candidate pushing information.
  • the present application provides an information push apparatus, where the apparatus includes: an obtaining unit configured to acquire candidate push information; and a determining unit configured to determine and push the candidate push information based on the pre-trained information identification model Corresponding identification information, a generating unit, configured to generate information to be pushed based on the candidate push information and the identifier information corresponding to the candidate push information, and a pushing unit configured to push the to-be-pushed information.
  • the determining unit includes: a website confirmation subunit, configured to confirm a website from which the candidate push information is derived; a feature information search subunit, configured to search for feature information of the website, and import feature information a subunit, configured to import the feature information into a pre-trained information identification model; the identifier information acquisition subunit, configured to acquire, according to the information identification model, identifier information corresponding to the feature information of the website, The identification information corresponding to the feature information of the website is used as the identification information corresponding to the candidate push information.
  • the feature information includes at least one of the following information of the website: server quantity information, domain name age information, ranking information, keyword ranking information, bounce rate information, outer chain number information, and flow information. , weight information, website organizer information.
  • the apparatus further includes: an information identification model establishing unit, comprising: a sample data obtaining subunit, configured to acquire feature information of the sample website and identification information corresponding to the determined feature information of the sample website; and the prediction identifier
  • the information obtaining sub-unit is configured to predict the identification information corresponding to the feature information of the sample website based on the initial model, and obtain the identification information corresponding to the feature information of the sample website predicted by the initial model, wherein the initial model is one of the following models: Support vector machine model, decision tree model, naive Bayesian model, logistic regression model; predictive identification information judging sub-unit, used to judge the identification information corresponding to the feature information of the sample website predicted by the initial model and the characteristics of the determined sample website Whether the identification information corresponding to the information is consistent; the parameter modification subunit is configured to determine, in the prediction identification information determining subunit, that the identification information corresponding to the feature information of the sample website predicted by the initial model is inconsistent with the identification information corresponding to the determined feature information of the sample website.
  • the identification information includes first identification information and second identification information; and the determining unit includes: a first selecting subunit, configured to be based on whether the searched information of the website is searched for And including a preset keyword, and selecting one of the first identification information and the second identification information as a label corresponding to the candidate push information Or the second selection subunit, configured to select one of the first identification information and the second identification information as the candidate to be pushed based on whether the information of the website is included in the acquired user report information set. Identification information corresponding to the information.
  • the information pushing method and apparatus obtains candidate push information, and then determines identification information corresponding to the candidate push information based on the pre-trained information identification model, and based on the candidate push information and the candidate push information.
  • the corresponding identification information generates the information to be pushed, and finally pushes the information to be pushed, thereby realizing the difference in the identification between the pushed information, so that the user obtains the information more efficiently.
  • FIG. 1 is an exemplary system architecture diagram to which the present application can be applied;
  • FIG. 2 is a flow chart of one embodiment of an information push method according to the present application.
  • FIG. 3 is a schematic diagram of an application scenario of an information pushing method according to the present application.
  • FIG. 5 is a schematic structural diagram of an embodiment of an information pushing apparatus according to the present application.
  • FIG. 6 is a schematic structural diagram of a computer system suitable for implementing a terminal device or a server of an embodiment of the present application.
  • FIG. 1 shows an example of an information pushing method or an information pushing device to which the present application can be applied.
  • system architecture 100 can include terminal devices 101, 102, 103, network 104, and server 105.
  • the network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105.
  • Network 104 may include various types of connections, such as wired, wireless communication links, fiber optic cables, and the like.
  • the user can interact with the server 105 over the network 104 using the terminal devices 101, 102, 103 to receive or transmit messages and the like.
  • Various communication client applications such as a web browser application, a shopping application, a search application, a map application, an instant communication tool, a mailbox client, a social platform software, and the like, may be installed on the terminal devices 101, 102, and 103.
  • the terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic The video specialist compresses the standard audio layer 3), MP4 (Moving Picture Experts Group Audio Layer IV) player, laptop portable computer and desktop computer, and the like.
  • MP3 players Motion Picture Experts Group Audio Layer III, dynamic The video specialist compresses the standard audio layer 3
  • MP4 Moving Picture Experts Group Audio Layer IV
  • the information pushing method provided by the embodiment of the present application is generally performed by the server 105. Accordingly, the information pushing device is generally disposed in the server 105.
  • the server 105 may also obtain candidate push information directly from other servers, push the information to be pushed to other servers, or the server 105 itself may store candidate push information.
  • the system architecture used in the application is also The above terminal devices 101, 102, 103 may not be involved.
  • terminal devices, networks, and servers in Figure 1 is merely illustrative. Depending on the implementation needs, there can be any number of terminal devices, networks, and servers.
  • the information pushing method includes the following steps:
  • Step 201 Acquire candidate push information.
  • the electronic device for example, the server shown in FIG. 1 on which the information push method runs may obtain the candidate push information by the following steps: , obtaining a user's search request; Then, the search result information is queried based on the search request. At this time, the search result information may be directly used as the candidate push information, or the screening condition may be set according to actual needs, the search result information may be filtered, and the filtered search result information is used as a candidate.
  • Push information For example, if the timeliness of candidate push information is high, you can set a time limit to filter the search results within the set time limit.
  • the electronic device for example, the server shown in FIG. 1 on which the information pushing method runs may also directly obtain search result information from the search server through a wired connection manner or a wireless connection manner, and the search result information is obtained.
  • candidate push information For example, the server shown in FIG. 1, the server shown in FIG. 1, the server shown
  • the candidate push information may also be acquired according to the user's account information and historical push information. For example, if the user's account information records the industry in which it works, the industry dynamic information of the above-mentioned industries may be used as candidate push information.
  • Step 202 Determine identification information corresponding to the candidate push information based on the pre-trained information identification model.
  • the identification information includes at least one of the following: image information, text information, and sound information.
  • the identification information can be used to indicate whether the candidate push information is secure and trustworthy. For example, if the source of the candidate push information is the website of the government agency, it is considered to be safe and trustworthy, and the corresponding identification information is positive, for example, the words "good” or "top” or the image may also be the letter "V". "" words or images, further, the words “V1", “V2", “V3” can be used to indicate the degree of credibility; if the source of the candidate push information is a website that has been reported, it is considered not to It is safe and trustworthy, and its corresponding identification information is negative, such as "non-premium", "not recommended” words or images. If it is not determined that the candidate push information corresponds to the positive identification information, the corresponding identification information may be set to be empty, and the identification information may be empty and the positive identification information may also be formed into a comparison.
  • the electronic device may query the feature information of the candidate push information in a preset database.
  • the candidate push information may be statistically analyzed and/or semantically analyzed, and at least one keyword, such as an organization name or a web address, may be extracted, and then the feature information corresponding to the key information is queried in a preset database based on the key information.
  • the website from which the candidate push information is derived may be obtained first, and the website from which the candidate push information is derived may be obtained through a search tool such as SEO (Search Engine Optimization) on the webmaster tool website that provides the website information. get on Search for the action and grab the information from the search results page as feature information.
  • SEO Search Engine Optimization
  • the feature information is imported into the pre-trained information identification model; according to the pre-trained correspondence relationship of the information identification model, the identification information corresponding to the feature information is obtained, and the identification information corresponding to the feature information is the candidate The identification information corresponding to the push information.
  • the feature information includes at least one of the following information of the website: server quantity information, domain name age information, ranking information, keyword ranking information, bounce rate information, outer chain number information, flow information, Weight information, information about the organizer of the website.
  • the ranking information of the website may be the ranking information of the website obtained from the Alexa Alexa ranking system.
  • Keyword ranking is a way to reflect the ranking of a page by the relevance of words, words, and phrases in search engine search results.
  • the natural ranking of keywords is generally the embodiment of automatic analysis and automatic ranking of all relevant webpage crawling results by search engines.
  • the search engine's website will provide keyword ranking information of the website.
  • the number of links in the website refers to the number of links imported from other websites to the website.
  • the commonly used external chain analysis tools can obtain the number of links of the website.
  • Website traffic is the number of visits to a website and is a measure of the number of users accessing a website and the number of pages viewed by the user.
  • the traffic information of the website may be historical traffic information or estimated traffic information.
  • the weight information of a website usually refers to the overall evaluation of a website by a search engine. As an example, Baidu weight, Google's PR (page rank, Google webpage level), or Sogou's SR (Sogou Rank) can be used.
  • the website registration information of the website may be searched first, and the first identification information and the second identifier are obtained based on whether the website registration information includes a preset keyword.
  • One of the information is selected as the identification information corresponding to the candidate push information.
  • the website registration information of the website may be searched, and the information corresponding to the field of the nature of the organizer in the record may be captured to determine whether the business order is included therein.
  • a keyword, a government agency, an army, or a social group if yes, determining that the identification information corresponding to the candidate push information is the first identification information, and the first identification information is positive identification information, which may be similar to “excellent” , "top" words or images.
  • the user report information set may be obtained, and the first identifier information and the second identifier information are selected based on whether the information about the website is included in the user report information set.
  • One is identification information corresponding to the candidate push information.
  • the user report information set may be historical report information of the user collected by the server, and the report information includes the reported item and the reported object information, and the reported object information may be the website address or the name of the website organizer.
  • the user report website 1 includes false content. After verification, the website 1 does include false content, and the server records the report information. If the website from which the candidate push information originates is the website 1, the identification information corresponding to the candidate push information is determined.
  • the second identification information is a negative identification information, and may be a word or image similar to “non-premium” or “not recommended”, or may be empty.
  • Step 203 Generate information to be pushed based on the candidate push information and the identifier information corresponding to the candidate push information.
  • the electronic device may combine the candidate push information and the identifier information corresponding to the candidate push information as the information to be pushed. For example, when the identification information is image information, the corresponding image may be added to the designated portion of the candidate push information.
  • Step 204 Push the information to be pushed.
  • FIG. 3 is a schematic diagram of an application scenario of the information pushing method according to the embodiment.
  • the user first initiates a search request, and the search keyword is “news”; after that, the information identification server may obtain search result information as candidate push information in the background, and extract feature information corresponding to the candidate push information; Then, the information identification server introduces the feature information of the candidate push information into the pre-trained information identification model, and determines that the identification information corresponding to the news website 1 and the news website 2 in the candidate push information is positive identification information, and the positive identification information is “excellent.
  • the word "" is combined with the candidate push information to generate the information to be pushed, and finally pushes the information to be pushed.
  • the user browses the search results, if there is a hover or click on the word "excellent", Part or all of the feature information is displayed according to actual needs by means of a floating window or the like.
  • the method provided by the foregoing embodiment of the present application generates the to-be-push information by determining the identification information corresponding to the candidate push information, and generating the information to be pushed based on the candidate push information and the identifier information corresponding to the candidate push information.
  • the difference in the identification between the pushed information makes the user more efficient in obtaining information.
  • the flow 400 of the information pushing method includes the following steps:
  • Step 401 Acquire candidate push information.
  • the candidate push information may be generated based on the search result information associated with the search operation, or generated based on the user's account information and historical push information.
  • Step 402 Obtain a website from which the candidate push information is derived.
  • the candidate push information directly includes the information of the website from which the source is derived, and the website name or the website address can be extracted by performing statistical analysis and/or semantic analysis on the candidate push information.
  • step 403 it is determined whether the nature of the website sponsoring unit is a public institution, a government agency, an army, or a social group.
  • the website record information database may be queried for the nature of the sponsor corresponding to the website from which the candidate push information is derived, and Table 1 shows some records in the record information database.
  • the above candidate push information can also be queried on the website filing information inquiry website.
  • the website from which the website is sourced obtains the information of the nature of the organizer corresponding to the website by means of crawling. If the website sponsoring unit of the website from which the candidate push information originates is a public institution, a government agency, an army or a social group, then the above is determined.
  • the identification information corresponding to the candidate push information is positive identification information, and proceeds to step 406; if not, proceeds to step 404.
  • the identification information is positive identification information.
  • Step 404 Determine whether the record of the website from which the candidate push information is originated or the record of the organizer of the website is included in the violation behavior database.
  • the records in the above-mentioned violation and trustworthy behavior database may be obtained based on the user's report history information, or may be obtained based on the national enterprise credit information publicity system or the publicized list of serious illegal and untrustworthy enterprises, if the violation behavior database includes the website from which the candidate push information originates.
  • the record or the record of the organizer of the website determines that the identification information corresponding to the candidate push information is negative identification information, and proceeds to step 406; if not, proceeds to step 405.
  • Step 405 Acquire feature information according to the candidate push information, and import the feature information into the information identification model.
  • the steps to establish an information identification model include:
  • sample data required for training the model includes feature information of the sample website and identification information corresponding to the determined feature information of the sample website.
  • the feature information of the sample website and the identification information corresponding to the determined feature information of the sample website may be obtained from the sample data set, and the sample data set may be manually set, or may be a website corresponding to the determined identification information, at the station.
  • the search result page of the long tool type website is obtained by fetching the corresponding feature information.
  • the official website of the Fortune 500 companies has been identified as “excellent”, search for the official website of the Fortune 500 companies on the webmaster tools website, and grab the official website of the Fortune 500 companies on the search results page.
  • Characteristic information will be the world's top 500 companies
  • the official website serves as a sample website, and the feature information of the official website of the Fortune 500 companies is used as the feature information of the sample website, and "excellent" is used as the identification information corresponding to the characteristic information of the determined sample website.
  • Obtaining the sample data required for training the model can also be obtained by counting the manner in which the user browses the record. For example, the identification information corresponding to the website repeatedly visited by a large number of users in a specific time period is determined as “excellent”, and such websites are used as Sample website.
  • the identification information corresponding to the feature information of the sample website is predicted, and the identification information corresponding to the feature information of the sample website predicted by the initial model is obtained, wherein the initial model is one of the following models: a support vector machine model , decision tree model, naive Bayesian model, logistic regression model.
  • the feature information of the sample website and the identification information corresponding to the determined feature information of the sample website are used as the training data of the initial model, and the parameters of the initial model are modified based on the training data, Obtaining the information identification model.
  • the LIBSVM software may be run.
  • the kernel function is determined to be a linear kernel.
  • the parameters that need to be selected and adjusted by the linear kernel are the penalty parameter C.
  • the weight parameter weight is used to adjust the weight of C of different categories of parameters. The weight can be set to positive and negative samples.
  • the penalty parameter C can generally range from 0.0001 to 10000, and the value of C can be adjusted according to the above training data.
  • the feature information is acquired according to the candidate push information, and the feature information is imported into the information identification model; the information identification model finds the corresponding identification information according to the pre-trained correspondence relationship.
  • Step 406 Generate to-be-push information based on the candidate push information and the identifier information corresponding to the candidate push information.
  • Step 407 Push the information to be pushed.
  • the error identification information and its corresponding feature data can be used as new training data to retrain the information identification model to further improve the information identification. The accuracy of the model.
  • the flow 400 of the information push method in the present embodiment highlights the step of determining the identification information as compared with the embodiment corresponding to FIG. Therefore, the solution described in this embodiment can introduce more relevant data for determining the identification information, thereby realizing the determination of the identification information with higher accuracy and more effective information push.
  • the present application provides an embodiment of an information pushing apparatus, and the apparatus embodiment corresponds to the method embodiment shown in FIG. Used in a variety of electronic devices.
  • the information pushing apparatus 500 described in this embodiment includes an obtaining unit 501, a determining unit 502, a generating unit 503, and a pushing unit 504.
  • the obtaining unit 501 is configured to acquire candidate push information;
  • the determining unit 502 is configured to determine, according to the pre-trained information identification model, identifier information corresponding to the candidate push information;
  • the generating unit 503 is configured to use the candidate push information.
  • the identification information corresponding to the candidate push information, generating the information to be pushed; and the pushing unit 504 is configured to push the information to be pushed.
  • the obtaining unit 501 of the information pushing device 500 can obtain candidate push information from the terminal or other server through a wired connection manner or a wireless connection manner.
  • the obtaining unit 501 acquires candidate push information
  • the information pushing device 500 is pre-trained with the information identification model, whereby the determining unit 502 of the information pushing device 500 can determine and candidate based on the pre-trained information identification model.
  • the identification information corresponding to the push information, the generating unit 503 may generate the to-be-pushed information based on the candidate push information and the identification information corresponding to the candidate push information, and the pushing unit 504 may push the information to be pushed generated by the generating unit 503.
  • the determining unit 502 includes: a website confirmation subunit, configured to confirm a website from which the candidate push information is derived; and a feature information search subunit, configured to search for feature information of the website, and feature information.
  • Import subunits for using the features The information is imported into the pre-trained information identification model; the identification information acquisition sub-unit is configured to obtain the identification information corresponding to the feature information of the website determined according to the information identification model, and the identifier corresponding to the feature information of the website The information is identification information corresponding to the candidate push information.
  • the feature information includes at least one of the following information of the website: server quantity information, domain name age information, ranking information, keyword ranking information, bounce rate information, outer chain number information, and flow information. , weight information, website organizer information.
  • the apparatus further includes: an information identification model establishing unit, comprising: a sample data obtaining subunit, configured to acquire feature information of the sample website and identification information corresponding to the determined feature information of the sample website; and the prediction identifier
  • the information obtaining sub-unit is configured to predict the identification information corresponding to the feature information of the sample website based on the initial model, and obtain the identification information corresponding to the feature information of the sample website predicted by the initial model, wherein the initial model is one of the following models: Support vector machine model, decision tree model, naive Bayesian model, logistic regression model; predictive identification information judging sub-unit, used to judge the identification information corresponding to the feature information of the sample website predicted by the initial model and the characteristics of the determined sample website Whether the identification information corresponding to the information is consistent; the parameter modification subunit is configured to determine, in the prediction identification information determining subunit, that the identification information corresponding to the feature information of the sample website predicted by the initial model is inconsistent with the identification information corresponding to the determined feature information of the sample website.
  • the identification information includes first identification information and second identification information
  • the determining unit 502 includes: a first selection subunit, configured to be based on the searched information of the website Whether the preset keyword is included, one of the first identification information and the second identification information is selected as the identification information corresponding to the candidate push information; or the second selection sub-unit is configured to report based on the obtained user Whether the information of the website is included in the information set, and selecting one of the first identification information and the second identification information as the identification information corresponding to the candidate pushing information.
  • the above information pushing device 500 also includes some of its His well-known structures, such as processors, memories, etc., are not shown in FIG. 5 in order to unnecessarily obscure the embodiments of the present disclosure.
  • FIG. 6 a block diagram of a computer system 600 suitable for use with a server of an embodiment of the present application is shown.
  • computer system 600 includes a central processing unit (CPU) 601 that can be loaded into a program in random access memory (RAM) 603 according to a program stored in read only memory (ROM) 602 or from storage portion 608. And perform various appropriate actions and processes.
  • RAM random access memory
  • ROM read only memory
  • RAM random access memory
  • various programs and data required for the operation of the system 600 are also stored.
  • the CPU 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604.
  • An input/output (I/O) interface 605 is also coupled to bus 604.
  • the following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a storage portion 608 including a hard disk or the like. And a communication portion 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the Internet.
  • Driver 610 is also coupled to I/O interface 605 as needed.
  • a removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory or the like, is mounted on the drive 610 as needed so that a computer program read therefrom is installed into the storage portion 608 as needed.
  • an embodiment of the present disclosure includes a computer program product comprising a computer program tangibly embodied on a machine readable medium, the computer program comprising program code for executing the method illustrated in the flowchart.
  • the computer program can be downloaded and installed from the network via communication portion 609, and/or installed from removable media 611.
  • the central processing unit (CPU) 601 the above-described functions defined in the method of the present application are performed.
  • each block in the flowchart or block diagram can represent a module, program segment, or code.
  • the module, program segment, or portion of code includes one or more executable instructions for implementing the specified logical functions.
  • the functions noted in the blocks may also occur in a different order than that illustrated in the drawings. For example, two successively represented blocks may in fact be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented in a dedicated hardware-based system that performs the specified function or operation. Or it can be implemented by a combination of dedicated hardware and computer instructions.
  • the units involved in the embodiments of the present application may be implemented by software or by hardware.
  • the described unit may also be provided in the processor, for example, as a processor including an acquisition unit, a determination unit, a generation unit, and a push unit.
  • the names of these units do not constitute a limitation on the unit itself in some cases.
  • the obtaining unit may also be described as “a unit that acquires candidate push information”.
  • the present application further provides a non-volatile computer storage medium, which may be a non-volatile computer storage medium included in the apparatus described in the foregoing embodiments; It may be a non-volatile computer storage medium that exists alone and is not assembled into the terminal.
  • the non-volatile computer storage medium stores one or more programs, when the one or more programs are executed by one device, causing the device to: obtain candidate push information; determine and match based on the pre-trained information identification model The identifier information corresponding to the candidate push information is generated; the to-be-push information is generated based on the candidate push information and the identifier information corresponding to the candidate push information; and the to-be-push information is pushed.

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Abstract

L'invention concerne un procédé et un dispositif de poussée d'informations, le procédé consistant : à acquérir des informations candidates en vue d'une poussée (201) ; en fonction d'un modèle d'identification d'informations préalablement formé, à déterminer des informations d'identification correspondant aux informations candidates en vue d'une poussée (202) ; à générer des informations à pousser en fonction des informations candidates en vue d'une poussée et des informations d'identification correspondant aux informations candidates en vue d'une poussée (203) ; à pousser les informations à pousser (204). La présente invention permet d'identifier d'une autre manière des informations en vue d'une poussée et permet à des utilisateurs d'acquérir des informations d'une manière plus efficace.
PCT/CN2016/087453 2016-01-15 2016-06-28 Procédé et dispositif de poussée d'informations WO2017121076A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201610029313.9A CN105718533A (zh) 2016-01-15 2016-01-15 信息推送方法和装置
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CN112766995A (zh) * 2019-10-21 2021-05-07 招商证券股份有限公司 物品推荐方法、装置、终端设备及存储介质
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