CN106911946B - Information data pushing method and device - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs
- H04N21/23424—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs involving splicing one content stream with another content stream, e.g. for inserting or substituting an advertisement
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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
- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0269—Targeted advertisements based on user profile or attribute
- G06Q30/0271—Personalized advertisement
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
- H04N21/25866—Management of end-user data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/262—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists
- H04N21/26208—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists the scheduling operation being performed under constraints
- H04N21/26241—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists the scheduling operation being performed under constraints involving the time of distribution, e.g. the best time of the day for inserting an advertisement or airing a children program
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/266—Channel or content management, e.g. generation and management of keys and entitlement messages in a conditional access system, merging a VOD unicast channel into a multicast channel
- H04N21/2668—Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/431—Generation of visual interfaces for content selection or interaction; Content or additional data rendering
- H04N21/4312—Generation of visual interfaces for content selection or interaction; Content or additional data rendering involving specific graphical features, e.g. screen layout, special fonts or colors, blinking icons, highlights or animations
- H04N21/4316—Generation of visual interfaces for content selection or interaction; Content or additional data rendering involving specific graphical features, e.g. screen layout, special fonts or colors, blinking icons, highlights or animations for displaying supplemental content in a region of the screen, e.g. an advertisement in a separate window
Abstract
The embodiment of the invention discloses an information data pushing method and device. The method comprises the following steps: acquiring attribution information and use habit data of a user; classifying the information data, the attribution information of the user and the using habit data according to a preset classification rule to obtain a data classification result; analyzing attribution information, use habit data and the data classification result of a target user to obtain the corresponding relation between the target user and the data classification result; and pushing the information data to the target user according to the corresponding relation. By the technical scheme, the technical problem that television information data pushing is not strong in pertinence is solved, and the technical effect that the television information data can be pushed in a targeted mode according to specific information such as attribution information and using habit data of a user is achieved, so that the utilization rate of the information data is improved, and the popularization value is improved.
Description
Technical Field
The embodiment of the invention relates to an advertisement publishing technology, in particular to an information data pushing method and device.
Background
With the development of smart televisions, television users are increasing, and television information data push such as television advertisements and television movie and television series propaganda becomes an indispensable part in the media field. Currently, in addition to advertising between television program slots, a variety of television information data in the form of pictures or videos may be presented to the user via software within the smart television and/or television set-top box.
In practical application, the pushing mode of the television information data is mainly to broadcast the information data to the user client through a television front end/television sub-front end system, so that the content and the presentation mode of the information data received by each user are consistent. The television information data broadcasting mode can not correspondingly distribute targeted advertisements according to the attributes of the users, so that the utilization rate of the television information data is low, the popularization value significance is low, the income of operators is influenced, and the watching experience of the television users is influenced.
Disclosure of Invention
The embodiment of the invention provides an information data pushing method and device, which are used for pushing information data to a television user in a targeted manner, and improving the utilization rate and the popularization value of the information data.
In a first aspect, an embodiment of the present invention provides an information data pushing method, which is applied to a television network, and includes the following steps:
acquiring attribution information and use habit data of a user;
classifying the information data, the attribution information of the user and the using habit data according to a preset classification rule to obtain a data classification result;
analyzing attribution information, use habit data and the data classification result of a target user to obtain the corresponding relation between the target user and the data classification result;
and pushing the information data to the target user according to the corresponding relation.
Optionally, the step of classifying the information data, the attribution information of the user, and the usage habit data according to a preset classification rule to obtain a data classification result includes:
classifying the information data according to a preset information data classification rule to obtain an information data classification result; and/or
According to a preset attribution region classification rule, performing region classification on attribution information of the user to obtain an attribution region classification result; and/or
And classifying the use habit data of the user according to a preset use habit classification rule to obtain a use habit data classification result.
Optionally, the step of analyzing the attribution information of the target user, the usage habit data, and the data classification result to obtain a corresponding relationship between the target user and the data classification result includes:
matching the attribution information of the target user with the attribution region classification result to obtain the corresponding relation between the target user and the attribution region classification result;
matching the use habit data of the target user with the use habit data classification result to obtain a corresponding relation between the target user and the use habit data classification result;
and obtaining the corresponding relation between the target user and the data classification result according to the corresponding relation between the target user and the attribution type classification result, the corresponding relation between the target user and the using habit data classification result and the information data classification result.
The step of matching the usage habit data of the target user with the classification result of the usage habit data to obtain the corresponding relationship between the target user and the classification result of the usage habit data comprises the following steps:
and sorting the use habit data of the target user according to the use habit data sorting result to obtain the use habit data sorting result of the target user.
The step of matching the usage habit data of the target user with the classification result of the usage habit data to obtain the corresponding relationship between the target user and the classification result of the usage habit data comprises the following steps:
sorting the using habit data sorting results to obtain using habit data sorting results;
and matching the use habit data of the target user with the use habit data classification and sorting result to obtain the corresponding relation between the target user and the use habit data classification and sorting result.
Optionally, the attribution information comprises an address and/or client identification information.
Optionally, the step of pushing the information data to the target user according to the corresponding relationship includes:
determining whether to mark the client identification information of the target user into the information data or not according to the corresponding relation;
and pushing the information data to the target user according to the client identification information of the target user.
Wherein the step of pushing the information data to the target user according to the client identification information of the target user comprises:
matching the client identification information marked in the information data with the client identification information of the target user;
and determining to reserve or delete the information data in the client of the target user according to the matching result.
In a second aspect, an embodiment of the present invention further provides an information data pushing apparatus, where the apparatus includes:
the user data acquisition module is used for acquiring attribution information and use habit data of a user;
the data classification result acquisition module is used for classifying the information data, the attribution information of the user and the use habit data according to a preset classification rule to obtain a data classification result;
the corresponding relation acquisition module is used for analyzing the attribution information, the use habit data and the data classification result of the target user to obtain the corresponding relation between the target user and the data classification result;
the information data pushing module is used for pushing the information data to the target user according to the corresponding relation;
optionally, the data classification result obtaining module includes:
the information data classification result acquisition submodule is used for classifying the information data according to a preset information data classification rule to obtain an information data classification result; and/or
The attribution region classification result obtaining submodule is used for carrying out region division on the attribution information of the user according to a preset attribution region classification rule to obtain an attribution region classification result; and/or
And the using habit data classification result acquisition submodule is used for classifying the using habit data of the user according to a preset using habit classification rule to obtain a using habit data classification result.
Optionally, the correspondence obtaining module includes:
the attribution region corresponding relation obtaining sub-module is used for matching attribution information of the target user with the attribution region classification result to obtain the corresponding relation between the target user and the attribution region classification result;
a habit data corresponding relation obtaining submodule, configured to match the habit data of the target user with the habit data classification result, so as to obtain a corresponding relation between the target user and the habit data classification result;
and the data classification result corresponding relation obtaining sub-module is used for obtaining the corresponding relation between the target user and the data classification result according to the corresponding relation between the target user and the attribution type classification result, the corresponding relation between the target user and the use habit data classification result and the information data classification result.
Wherein, the using habit data corresponding relation obtaining submodule comprises:
the using habit data sorting result acquiring unit is used for sorting the using habit data sorting results to obtain using habit data sorting results;
and the using habit data corresponding relation obtaining unit is used for obtaining the corresponding relation between the target user and the using habit data classification result according to the using habit data of the target user and the using habit data classification sequencing result.
Further, the usage habit data correspondence obtaining unit is specifically configured to:
sorting the use habit data of the target user according to the use habit data sorting result to obtain the use habit data sorting result of the target user;
establishing a mapping relation between the use habit data classification sorting result and the use habit data classification sorting result of the information pushing user as a corresponding relation between the target user and the use habit data classification result; or
And matching the use habit data of the target user with the use habit data classification and sorting result to obtain the corresponding relation between the target user and the use habit data classification and sorting result.
Optionally, the attribution information comprises an address and/or client identification information.
Optionally, the information data pushing module includes:
the client identification information marking submodule is used for determining whether to mark the client identification information of the target user into the information data or not according to the corresponding relation;
and the information data pushing submodule is used for pushing the information data to the target user according to the client identification information of the target user.
The information data pushing submodule is specifically configured to:
matching the client identification information marked in the information data with the client identification information of the target user;
and determining to reserve or delete the information data in the client of the target user according to the matching result.
According to the information data pushing method and device provided by the embodiment of the invention, the attribution information and the use habit data of the user including the target user are obtained, and the information data, the attribution information of the user and the use habit data are classified according to the preset classification rule to obtain the data classification result; analyzing attribution information, use habit data and the data classification result of the target user to obtain the corresponding relation between the target user and the data classification result; according to the corresponding relation, the technical scheme for pushing the information data to the target user solves the technical problem that television information data pushing is not strong in pertinence, and achieves the technical effect that the television information data can be pushed in a targeted mode according to specific information such as attribution information and using habit data of the user, and therefore the utilization rate and the popularization value of the information data are improved.
Drawings
Fig. 1 is a flowchart of an information data pushing method according to an embodiment of the present invention;
fig. 2 is a flowchart of an information data pushing method according to a second embodiment of the present invention;
fig. 3 is a flowchart of an information data pushing method according to a third embodiment of the present invention;
fig. 4 is a flowchart of an information data pushing method according to a fourth embodiment of the present invention.
Fig. 5 is a schematic structural diagram of an information data pushing apparatus according to a fifth embodiment of the present invention.
Fig. 6a, 6b, and 6c are schematic structural diagrams of an information data pushing apparatus according to a fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
In order to push the television information data in a targeted manner, the use habit data of the television user needs to be known first, namely the attribution information and the use habit data of at least one television user are obtained; then, what information data the target user tends to need is analyzed, that is, statistical analysis is performed on the attribution information, the usage habit data and the information data to be pushed, the attribution information and the usage habit data of the user with the information to be pushed and the result of the statistical analysis are further analyzed, a statistical analysis result having a certain corresponding relationship with the behavior habit of the user is obtained, and the information data is pushed according to the corresponding relationship.
Example one
Fig. 1 is a flowchart of an information data pushing method according to an embodiment of the present invention, where the present embodiment is applicable to information data pushing for a television, the method may be executed by an information data pushing apparatus, such as a television front-end/sub-front-end system or a network set-top box, and the method may be implemented by software and/or hardware deployed in the information data pushing apparatus. The method specifically comprises the following steps:
s100, acquiring attribution information and using habit data of the user.
The home information may be home information of the user. For example, the detailed home address of the user may be, the cell information of the user may also be, a unit address of the user may be, and the like, which may be obtained directly through a home address or a work unit address registered when the user opens the television service, or may be obtained through information such as a user television set-top box, a service purchase account, or an online shopping account associated with the user address, and the obtaining manner of the home information is not limited in this embodiment.
The usage habit data can be habit data of the user using the intelligent television. For example, it may be user history and/or data of currently viewed television channels, television program type data or user history and/or data of currently engaged in television shopping, etc. The acquisition of the use habit data may be automatically acquired through the smart television and/or the smart set-top box, or may be manually acquired through other manners, and the acquisition manner of the use habit data is not limited in this embodiment. For example, for the acquisition of the shopping data of the user, the historical shopping data of the user can be automatically acquired directly according to the record of the shopping software in the intelligent set top box, and the shopping data can also be manually acquired according to the online shopping account number associated with the attribution information of the user.
Specifically, attribution information and usage habit data of television users who can obtain information are obtained, for example, attribution information and usage habit data of television users in the whole country, province and city or a certain region are obtained, and basic big data for performing next step information data push is obtained.
It should be noted that the user may not include the target user, and may include the target user. The target user may be a push object of information data, which may be a single user, or multiple users with certain association, that is, the method of this embodiment may perform information push to a single user, or may perform information push to multiple users with certain association, for example, may perform information push to users belonging to a same cell, or perform information push to users belonging to a same work unit, and the like. The plurality of users having a certain association can be determined by the attribution information and the usage habit data of each user, and the following description can be specifically seen.
The target users are included because the information of the users can be combined with the big data information of multiple users and the specific information of the target users to carry out more targeted information data push.
S200, classifying the information data, the attribution information of the user and the using habit data according to a preset classification rule to obtain a data classification result.
The preset classification rule is a predetermined classification rule for data classification, which is not limited to a specific classification rule, and may include at least one or more of an information data classification rule, a home zone classification rule, and a usage habit classification rule, for example.
Specifically, according to a preset classification rule, classifying one or more of information data to be pushed, attribution information of a user and use habit data of the user to obtain a data classification result. The data classification result can be used for screening and matching of subsequent information data and target users.
For example, the process of obtaining the data classification result in step S200 may be:
classifying the information data according to a preset information data classification rule to obtain an information data classification result; and/or
According to a preset attribution region classification rule, performing region classification on attribution information of the user to obtain an attribution region classification result; and/or
And classifying the use habit data of the user according to a preset use habit classification rule to obtain a use habit data classification result.
That is, the data classification result may include one or more of an information data classification result, an attribution region classification result, and a usage habit data classification result, and the subsequent information data and the target user may be screened and matched according to one or more of the classification results.
For example, if the target user includes one user or a plurality of users, when a plurality of users are determined, screening may be performed according to the obtained classification result of the usage habit data, so as to determine a plurality of users having the same usage habit category; or determining a plurality of users belonging to the same region according to the classification result of the belonging region; and determining a plurality of users with the same use habit type in the same region according to the use habit data classification result and the attribution region classification result.
For another example, for the pushing of the information data, the pushing of the regions can be performed according to the attribution region classification result of the user; information data corresponding to the information data categories can be pushed to the user according to the using habit data classification result; the information data may be pushed according to the information data type in a region.
S300, analyzing attribution information, using habit data and data classification results of the target user to obtain the corresponding relation between the target user and the data classification results.
Step S300 is used to determine how to perform the information pushing, that is, determine the pushing basis for performing the information data pushing. Specifically, the attribution information and the usage habit data of the target user are subjected to matching analysis with the data classification result determined in step S200, and a corresponding relationship between the target user and the data classification result is determined.
It should be noted that the corresponding relationship here includes a corresponding relationship between the target user and at least one classification result in the data classification results, that is, only a corresponding relationship between the target user and an attribution region classification result in the data classification results may be included, only a corresponding relationship between the target user and a usage habit data classification result in the data classification results may be included, a comprehensive corresponding relationship between the target user and an attribution region classification result, a usage habit data classification result, and an information data classification result in the data classification results may also be included, and the specific corresponding relationship may be appropriately selected according to a push request determined by an actual application requirement, which is not limited in this embodiment.
S400, pushing the information data to the target user according to the corresponding relation.
Specifically, according to the corresponding relationship determined in step S300, the corresponding information data is pushed to the target user. For example, the information data may be pushed directly according to the corresponding category in step S300; or sorting the obtained sorting results, and selectively pushing the information data according to the sorting results; the user information to be pushed can be associated to the information data according to the corresponding relation, then the associated information data is pushed to a plurality of users including the target user, and certain rules are screened according to the associated user information in the information data.
In the information data pushing method provided by this embodiment, the attribution information and the usage habit data of the user including the target user are acquired, and the information data, the attribution information of the user, and the usage habit data are classified according to the preset classification rule, so as to obtain a data classification result; analyzing attribution information, use habit data and the data classification result of the target user to obtain the corresponding relation between the target user and the data classification result; according to the corresponding relation, the technical scheme for pushing the information data to the target user solves the technical problem that the television information data pushing is not strong in pertinence, and achieves the technical effect that the television information data can be pushed in a targeted mode according to specific information such as attribution information and using habit data of the target user, and therefore the utilization rate and the popularization value of the information data are improved.
Example two
Fig. 2 is a flowchart of an information data pushing method according to a second embodiment of the present invention, and in this embodiment, based on the above-mentioned embodiment, further optimization is performed on the action of "classifying the information data, the attribution information of the user, and the usage habit data according to a preset classification rule to obtain a data classification result" in step S200, and the action of "analyzing the attribution information of the target user, the usage habit data, and the data classification result to obtain a corresponding relationship between the target user and the data classification result" in step S300. The same or corresponding terms as those in the above embodiments are not explained in detail in this embodiment.
An information data pushing method provided by a second embodiment of the present invention is described below with reference to fig. 2, where the method of the present embodiment includes:
s100, acquiring attribution information and using habit data of the user.
S210, classifying the information data according to a preset information data classification rule to obtain an information data classification result.
The preset information data classification rule is a preset rule for classifying the information data to be pushed, and may be an industry classification rule of different levels or an industry product classification rule of different levels, and the like, and any general rule is not limited herein as long as the general rule can classify the information data.
Specifically, according to a preset information data classification rule, classification of information data to be pushed is performed to obtain an information data classification result. The information data classification result may be a classification result of a first level, a classification result of a more detailed category such as a second level and a third level, or a multi-level classification result of multiple levels, and a specific classification level and a classification level standard may be selected according to actual needs, which is not limited in this embodiment.
S220, according to a preset attribution region classification rule, performing region classification on the attribution information of the user to obtain an attribution region classification result.
The preset attribution region classification rule is a preset rule for classifying attribution information, and may be a general rule for performing region classification in each country and region.
Specifically, according to a preset attribution region classification rule, attribution information of the user is subjected to region classification to obtain an attribution region classification result. As described in step S210, the classification result of the home region may also include a multi-level and/or multi-level classification result, which may be determined according to actual needs.
And S230, classifying the use habit data of the user according to a preset use habit classification rule to obtain a use habit data classification result.
The preset usage habit classification rule is a preset rule for classifying the usage habit data, and the preset usage habit classification rule is correspondingly consistent with the information data classification rule.
Specifically, according to a preset using habit classification rule, classifying the using habit data of the user to obtain a using habit data classification result. Similarly, the habit data classification result may also include multi-level and/or multi-level classification results, which may be determined according to actual needs. However, in order to better establish the corresponding relationship between different data so as to more specifically push the information data, the classification level and the classification level of the data classification result using habit may be consistent with the classification level and the classification level of the information data classification result in step S210.
S310, matching the attribution information of the target user with the attribution region classification result to obtain the corresponding relation between the target user and the attribution region classification result.
Specifically, the attribution information of the target user is matched with the attribution region classification result in the data classification result, and the attribution region of the target user is determined, so that the corresponding relation between the target user and the attribution region classification result is obtained. The information is pushed according to the corresponding relation of the attribution region, so that the target user can know the information of the object which the target user wants to pay attention to in time, the target user can conveniently know the information to other users in the same region who have been eagerly contacted with the information data covering object, and the actual popularization value of the information data is improved.
For example, for the same cell, the information data coverage object to be pushed is a new energy automobile, and after the information data is pushed according to the corresponding relationship of the attribution region, the target user can conveniently consult the user who has purchased or used the new energy automobile in the same cell for the use experience or purchasing experience of the new energy automobile, and the like, so that the cluster consumption effect is more easily formed.
And S320, matching the use habit data of the target user with the use habit data classification result to obtain the corresponding relation between the target user and the use habit data classification result.
Specifically, the usage habit data of the target user is matched with the usage habit data classification result in the data classification result, the usage habit category of the target user is determined, and the corresponding relation between the target user and the usage habit data classification result is obtained.
S330, obtaining the corresponding relation between the target user and the data classification result according to the corresponding relation between the target user and the attribution type classification result, the corresponding relation between the target user and the use habit data classification result and the information data classification result.
Specifically, after the attribution region and the usage habit category of the target user are determined in the previous step S310 and the previous step S320, the attribution region and the usage habit category of the target user are matched with the information data classification result to obtain an information data category corresponding to and consistent with the usage habit category of the target user, that is, the corresponding relationship between the target user and the data classification result is obtained. Therefore, the corresponding relation between the target user and the three classification results is obtained, and the subsequent information data can be pushed according to the comprehensive corresponding relation.
For example, the information data corresponding to the target user usage habit data category in the target user home region range is pushed to the target user.
S400, pushing information data to the target user according to the corresponding relation.
In the information data pushing method provided by this embodiment, by acquiring attribution information and usage habit data of a user including a target user, and classifying the information data, the attribution information of the user, and the usage habit data according to a preset information data classification rule, a preset attribution region classification rule, and a preset usage habit classification rule, three types of data classification results of an information data classification result, an attribution region classification result, and a usage habit data classification result are obtained; matching and analyzing the attribution information, the use habit data and the classification results of the three types of data of the target user to obtain the corresponding relation between the target user and the classification results of the three types of data; according to all the corresponding relations, the technical scheme for pushing the information data to the target user solves the technical problem that television information data pushing is not strong in pertinence, achieves corresponding more targeted information data pushing according to the attribution region and the using habit data of the user, and achieves the technical effects of more targeted television information data pushing and further improving the utilization rate and the popularization value of the information data.
EXAMPLE III
Fig. 3 is a flowchart of an information data pushing method according to a third embodiment of the present invention, and in this embodiment, based on the above embodiment, the action of "matching the usage habit data of the target user with the usage habit data classification result to obtain the corresponding relationship between the target user and the usage habit data classification result" in step S320 is further optimized. The same or corresponding terms as those in the above embodiments are not explained in detail in this embodiment.
An information data pushing method provided by a third embodiment of the present invention is described below with reference to fig. 3, where the method of the present embodiment includes:
s100, acquiring attribution information and using habit data of a user, wherein the user comprises a target user.
S210, classifying the information data according to a preset information data classification rule to obtain an information data classification result.
S220, according to a preset attribution region classification rule, performing region classification on attribution information of the user to obtain an attribution region classification result.
And S230, classifying the use habit data of the user according to a preset use habit classification rule to obtain a use habit data classification result.
S310, matching the attribution information of the target user with the attribution region classification result to obtain the corresponding relation between the target user and the attribution region classification result.
S321, sorting the using habit data sorting results to obtain using habit data sorting results.
Specifically, the usage habit data classification results obtained in step S230 are sorted according to a preset order, and usage habit data classification sorting results are obtained, where the usage habit data classification sorting results do not limit the basis of sorting and sorting rules such as ascending and descending order.
S322, obtaining the corresponding relation between the target user and the classification result of the using habit data according to the using habit data of the target user and the classification result of the using habit data.
Specifically, after obtaining the usage habit data sorting result in step S321, the usage habit data of the target user may be matched with the usage habit data sorting result, so as to establish a corresponding relationship between the target user and the usage habit data sorting result.
Exemplarily, step S322 may further specifically be:
sorting the use habit data of the target user according to the use habit data sorting result to obtain the use habit data sorting result of the target user;
and establishing a mapping relation between the use habit data classification and sorting result and the use habit data classification and sorting result of the information pushing user as a corresponding relation between the target user and the use habit data classification result.
Specifically, the usage habit data of the target user is sorted according to the same sorting basis and arrangement rule as the classification sorting result of the usage habit data obtained in step S321, so as to obtain a classification sorting result of the usage habit data of the target user. The sorting result of the using habit data and the sorting result of the using habit data of the target user can be in corresponding and consistent relation or in incomplete corresponding relation. Then, a mapping relation between the use habit data sorting result and the use habit data sorting result of the target user is established, and the mapping relation is determined as the corresponding relation between the target user and the use habit data sorting result.
For example, the usage habit categories with the corresponding and consistent sorting order in the two sorting result mapping relations can be determined as the corresponding relations; the usage habit categories which are not in the usage habit data classification sorting result of the target user and are in the front of the usage habit data classification sorting result in the mapping relation of the two sorting results can also be determined as the corresponding relation; and determining the use habit categories ranked in the later order in the use habit category ranking result of the target user as the corresponding relation and the like, wherein the use habit data is ranked in the front order in the mapping relation of the two ranking results, and the use habit categories are ranked in the later order in the use habit category ranking result of the target user. The advantage of such an arrangement is that information data which is always concerned by or is lacking can be pushed to the target user according to the big use habit data of a plurality of users and the individual use habit data of the target user, so that the pushing range of the information data can be further expanded.
Exemplarily, step S322 may further specifically be:
and determining the corresponding relation between the target user and the use habit data classification and sorting result according to the use habit data classification and sorting result.
Specifically, the corresponding relation between the target user and the use habit data sorting result is directly determined according to the use habit data sorting result. For example, the usage habit categories sorted to the front or the back in the usage habit data sorting result may be used as the corresponding relationship according to the ascending or descending sorting rule of the usage habit data sorting result. The method has the advantages that the information data which are much concerned by most users can be pushed to the target users according to the big data of the use habits of the users, and therefore the pushing range of the information data can be further expanded.
S330, obtaining the corresponding relation between the target user and the data classification result according to the corresponding relation between the target user and the attribution type classification result, the corresponding relation between the target user and the use habit data classification result and the information data classification result.
S400, pushing information data to the target user according to the corresponding relation.
In the information data pushing method provided by this embodiment, by acquiring attribution information and usage habit data of a user including a target user, and classifying the information data, the attribution information of the user, and the usage habit data according to a preset information data classification rule, a preset attribution region classification rule, and a preset usage habit classification rule, three types of data classification results of an information data classification result, an attribution region classification result, and a usage habit data classification result are obtained; matching and analyzing the attribution information, the use habit data and the classification results of the three types of data of the target user, sequencing the use habit data classification results on the basis of obtaining the corresponding relation between the target user and the attribution region classification results, determining the corresponding relation between the target user and the use habit data classification results according to the obtained use habit data classification sequencing results and the use habit data of the target user, and further obtaining the corresponding relation between the target user and the information data classification results; according to all the corresponding relations, the technical scheme for pushing the information data to the target user solves the technical problem that the pushing pertinence of the television information data is not strong, achieves the purpose of selectively pushing the corresponding information data according to the attribution region and the use habit data classification and sequencing result of the target user, achieves the technical effects of pushing the television information data more pertinently by the user, and further improves the utilization rate, the popularization range and the popularization value of the information data.
Example four
Fig. 4 is a flowchart of an information data pushing method according to a fourth embodiment of the present invention, and in this embodiment, based on the foregoing embodiments, the action of "attribution information" and step S400 "pushing information data to a target user according to a corresponding relationship" is further optimized. The same or corresponding terms as those in the above embodiments are not explained in detail in this embodiment.
Next, an information data pushing method provided by a fourth embodiment of the present invention is described with reference to fig. 4, where the method of this embodiment includes:
s100, acquiring attribution information and using habit data of a user, wherein the attribution information comprises an address and/or client identification information.
The client identification information may be any client-related information that can identify the client, for example, a serial number or a model number of the user client.
Specifically, attribution information and usage habit data of the user are obtained, and the attribution information may include a user address or user client identification information, or may include both the user address and the user client identification information. The user client identification information is associated with the attribution information of the user, for example, when the client is sold, the identification information of the client and the purchaser information are correspondingly registered, or when the client is installed and used, the user registers personal information to activate the client, and the like.
S200, classifying the information data, the attribution information of the user and the using habit data according to a preset classification rule to obtain a data classification result.
S300, analyzing attribution information, using habit data and data classification results of the target user to obtain the corresponding relation between the target user and the data classification results.
S410, determining whether to mark the client identification information of the target user into the information data according to the corresponding relation.
Specifically, according to the correspondence determined in step S300, it can be determined whether the target user has a correspondence with the information data to be pushed, that is, whether the information data to be pushed is suitable for being pushed to the target user, and whether the client identification information of the target user is marked in the information data is determined according to the result. If it is determined that the information data to be pushed is suitable for the target user according to the corresponding relationship in step S300, the client identification information of the target user is marked in the information data. On the contrary, if it is determined that the information data to be pushed is not suitable for the target user according to the corresponding relationship in step S300, the client identification information of the non-target user is marked into the information data.
And S420, pushing information data to the target user according to the client identification information of the target user.
Specifically, after the client identification information of the target user is marked into the information data in step S410, the information data may be pushed to the target user according to the client identification information marked in the information data.
Exemplarily, step S420 may specifically be:
matching the client identification information marked in the information data with the client identification information of the target user;
and determining to reserve or delete the information data in the client of the target user according to the matching result.
Specifically, the information data is pushed to the user client, and then the client identification information marked in the information data is matched with the client identification information of the target user. And if the client identification information is successfully matched, downloading the information data and keeping the information data in the client of the target user so as to receive and watch the information data by the target user. If the client identification information fails to be matched, the information data is not downloaded and kept in the client of the target user, but the information data is discarded aiming at the client of the target user, so that the target user is ensured not to receive and see the information data. Therefore, the information data is directly reserved or deleted according to the client identification information of the target user, the information data received by the target user can be quickly determined to be interesting, but not a plurality of irrelevant information data which may make the target user boring, and the user experience of the intelligent television can be further improved.
In the information data pushing method provided by this embodiment, the attribution information and the usage habit data of the user including the target user are acquired, and the information data, the attribution information of the user, and the usage habit data are classified according to the preset classification rule, so as to obtain a data classification result; analyzing attribution information, use habit data and the data classification result of the target user to obtain the corresponding relation between the target user and the data classification result; according to the corresponding relation, whether the client identification information of the target user is marked in the information data or not is determined, and the technical scheme of pushing the information data to the target user according to the client identification information of the target user is adopted, so that the technical problem that the television information data pushing is not strong in pertinence is solved, the television information data can be pushed in a targeted mode according to the user information, and the technical effects of improving the utilization rate and the popularization value of the information data are achieved.
The following is an embodiment of the information data pushing apparatus provided in an embodiment of the present invention, the information data pushing apparatus and the information data pushing method in the foregoing embodiments belong to the same inventive concept, and details that are not described in detail in the embodiment of the information data pushing apparatus may refer to the embodiment of the information data pushing method.
EXAMPLE five
Fig. 5 is a schematic structural diagram of an information data pushing apparatus provided in this embodiment, and explanations of terms that are the same as or correspond to any of the above embodiments in this embodiment are not repeated herein.
The apparatus may include:
a user data obtaining module 510, configured to obtain attribution information and usage habit data of the user.
A data classification result obtaining module 520, configured to classify the information data, the attribution information of the user obtained by the user data obtaining module 510, and the usage habit data according to a preset classification rule, so as to obtain a data classification result.
A corresponding relationship obtaining module 530, configured to analyze the attribution information of the target user, the usage habit data, and the data classification result obtained by the data classification result obtaining module 520, to obtain a corresponding relationship between the target user and the data classification result.
The information data pushing module 540 is configured to push information data to the target user according to the corresponding relationship obtained by the corresponding relationship obtaining module 530.
Optionally, referring to fig. 6a, the data classification result obtaining module 520 includes:
the information data classification result obtaining submodule 521 is configured to perform category division on the information data according to a preset information data classification rule to obtain an information data classification result; and/or
The attribution region classification result obtaining sub-module 522 is configured to perform region classification on the attribution information of the user according to a preset attribution region classification rule to obtain an attribution region classification result; and/or
The usage habit data classification result obtaining sub-module 523 is configured to perform category classification on the usage habit data of the user according to a preset usage habit classification rule to obtain a usage habit data classification result.
Optionally, referring to fig. 6b, the correspondence obtaining module 530 includes:
an attribution region corresponding relation obtaining sub-module 531, configured to match attribution information of the target user with an attribution region classification result, so as to obtain a corresponding relation between the target user and the attribution region classification result;
a usage habit data corresponding relation obtaining sub-module 532, configured to match the usage habit data of the target user with the usage habit data classification result to obtain a corresponding relation between the target user and the usage habit data classification result;
the data classification result corresponding relation obtaining sub-module 533 is configured to obtain a corresponding relation between the target user and the data classification result according to the corresponding relation between the target user and the attribution type classification result, the corresponding relation between the target user and the usage habit data classification result, and the information data classification result.
The habit data correspondence obtaining sub-module 532 includes:
a used habit data sorting result obtaining unit 5321, configured to sort the used habit data sorting results to obtain a used habit data sorting result;
the usage habit data corresponding relationship obtaining unit 5322 is configured to obtain a corresponding relationship between the target user and the usage habit data classification result according to the usage habit data of the target user and the classification and sorting result of the usage habit data.
Further, the usage habit data correspondence obtaining unit 5322 is specifically configured to:
sorting the use habit data of the target user according to the use habit data sorting result to obtain the use habit data sorting result of the target user;
establishing a mapping relation between the use habit data classification sorting result and the use habit data classification sorting result of the information pushing user as a corresponding relation between a target user and the use habit data classification result; or
And matching the use habit data of the target user with the use habit data classification and sequencing result to obtain the corresponding relation between the target user and the use habit data classification and sequencing result.
Optionally, the home information comprises address and/or client identification information.
Optionally, referring to fig. 6c, the information data pushing module 540 includes:
and the client identification information marking sub-module 541 is configured to determine whether to mark the client identification information of the target user in the information data according to the correspondence.
And the information data pushing submodule 542 is configured to push information data to the target user according to the client identification information of the target user.
The information data pushing submodule 542 is specifically configured to:
matching the client identification information marked in the information data with the client identification information of the target user;
and determining to reserve or delete the information data in the client of the target user according to the matching result.
Through the information data pushing device in the fifth embodiment of the invention, the technical problem that television information data pushing does not have user pertinence is solved, and the technical effects that television information data can be pushed by a user pertinence, so that the utilization rate of the information data and the popularization value are improved are achieved.
The information data pushing device provided by the embodiment of the invention can execute the information data pushing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the specific embodiments described herein. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (6)
1. An information data pushing method applied to a television network is characterized by comprising the following steps:
acquiring attribution information and use habit data of a user;
classifying the information data, the attribution information of the user and the using habit data according to a preset classification rule to obtain a data classification result;
analyzing attribution information, use habit data and the data classification result of a target user to obtain the corresponding relation between the target user and the data classification result;
pushing the information data to the target user according to the corresponding relation;
the step of classifying the information data, the attribution information of the user and the using habit data according to a preset classification rule to obtain a data classification result comprises the following steps:
classifying the information data according to a preset information data classification rule to obtain an information data classification result;
according to a preset attribution region classification rule, performing region classification on attribution information of the user to obtain an attribution region classification result;
classifying the use habit data of the user according to a preset use habit classification rule to obtain a use habit data classification result;
wherein, the step of analyzing the attribution information, the usage habit data and the data classification result of the target user to obtain the corresponding relationship between the target user and the data classification result comprises:
matching the attribution information of the target user with the attribution region classification result to obtain the corresponding relation between the target user and the attribution region classification result;
matching the use habit data of the target user with the use habit data classification result to obtain a corresponding relation between the target user and the use habit data classification result;
obtaining the corresponding relation between the target user and the data classification result according to the corresponding relation between the target user and the attribution type classification result, the corresponding relation between the target user and the using habit data classification result and the information data classification result;
the step of matching the usage habit data of the target user with the classification result of the usage habit data to obtain the corresponding relationship between the target user and the classification result of the usage habit data comprises:
sorting the using habit data sorting results to obtain using habit data sorting results;
obtaining the corresponding relation between the target user and the use habit data classification result according to the use habit data of the target user and the use habit data classification sequencing result;
the step of obtaining the corresponding relation between the target user and the use habit data classification result according to the use habit data of the target user and the use habit data classification sequencing result comprises the following steps:
sorting the use habit data of the target user according to the use habit data sorting result to obtain the use habit data sorting result of the target user;
establishing a mapping relation between the use habit data classification sorting result and the use habit data classification sorting result of the target user, wherein the mapping relation is used as a corresponding relation between the target user and the use habit data classification result; or
And matching the use habit data of the target user with the use habit data classification and sorting result to obtain the corresponding relation between the target user and the use habit data classification and sorting result.
2. The method of claim 1, wherein the home information comprises address and/or client identification information.
3. The method according to claim 2, wherein the step of pushing the information data to the target user according to the correspondence comprises:
determining whether to mark the client identification information of the target user into the information data or not according to the corresponding relation;
and pushing the information data to the target user according to the client identification information of the target user.
4. The method of claim 3, wherein the step of pushing the information data to the target user according to the client identification information of the target user comprises:
matching the client identification information marked in the information data with the client identification information of the target user;
and determining to reserve or delete the information data in the client of the target user according to the matching result.
5. An information data pushing device applied to a television network is characterized by comprising:
the user data acquisition module is used for acquiring attribution information and use habit data of a user;
the data classification result acquisition module is used for classifying the information data, the attribution information of the user and the use habit data according to a preset classification rule to obtain a data classification result;
the corresponding relation acquisition module is used for analyzing the attribution information, the use habit data and the data classification result of the target user to obtain the corresponding relation between the target user and the data classification result;
the information data pushing module is used for pushing the information data to the target user according to the corresponding relation;
wherein, the data classification result acquisition module comprises:
the information data classification result acquisition submodule is used for classifying the information data according to a preset information data classification rule to obtain an information data classification result;
the attribution region classification result obtaining submodule is used for carrying out region division on the attribution information of the user according to a preset attribution region classification rule to obtain an attribution region classification result;
the using habit data classification result obtaining submodule is used for classifying the using habit data of the user according to a preset using habit classification rule to obtain a using habit data classification result;
the corresponding relation obtaining module comprises:
the attribution region corresponding relation obtaining sub-module is used for matching attribution information of the target user with the attribution region classification result to obtain the corresponding relation between the target user and the attribution region classification result;
a habit data corresponding relation obtaining submodule, configured to match the habit data of the target user with the habit data classification result, so as to obtain a corresponding relation between the target user and the habit data classification result;
a data classification result corresponding relation obtaining sub-module, configured to obtain a corresponding relation between the target user and the data classification result according to the corresponding relation between the target user and the attribution type classification result, the corresponding relation between the target user and the usage habit data classification result, and the information data classification result;
the using habit data corresponding relation obtaining submodule comprises:
the using habit data sorting result acquiring unit is used for sorting the using habit data sorting results to obtain using habit data sorting results;
a usage habit data corresponding relation obtaining unit, configured to obtain a corresponding relation between the target user and the usage habit data classification result according to the usage habit data of the target user and the usage habit data classification sorting result;
the usage habit data corresponding relation obtaining unit is specifically configured to:
sorting the use habit data of the target user according to the use habit data sorting result to obtain the use habit data sorting result of the target user;
establishing a mapping relation between the use habit data classification sorting result and the use habit data classification sorting result of the target user, wherein the mapping relation is used as a corresponding relation between the target user and the use habit data classification result; or
And matching the use habit data of the target user with the use habit data classification and sorting result to obtain the corresponding relation between the target user and the use habit data classification and sorting result.
6. The apparatus of claim 5,
the attribution information comprises an address and/or client identification information;
the information data pushing module comprises:
the client identification information marking submodule is used for determining whether to mark the client identification information of the target user into the information data or not according to the corresponding relation;
the information data pushing submodule is used for matching the client identification information marked in the information data with the client identification information of the target user;
and determining to reserve or delete the information data in the client of the target user according to the matching result.
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