CN111711828B - Information processing method and device and electronic equipment - Google Patents
Information processing method and device and electronic equipment Download PDFInfo
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- CN111711828B CN111711828B CN202010422378.6A CN202010422378A CN111711828B CN 111711828 B CN111711828 B CN 111711828B CN 202010422378 A CN202010422378 A CN 202010422378A CN 111711828 B CN111711828 B CN 111711828B
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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
- H04N21/21—Server components or server architectures
- H04N21/218—Source of audio or video content, e.g. local disk arrays
- H04N21/2187—Live feed
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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
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
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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
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
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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
- H04N21/47—End-user applications
- H04N21/478—Supplemental services, e.g. displaying phone caller identification, shopping application
- H04N21/4781—Games
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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
- H04N21/47—End-user applications
- H04N21/478—Supplemental services, e.g. displaying phone caller identification, shopping application
- H04N21/47815—Electronic shopping
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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
- H04N21/47—End-user applications
- H04N21/478—Supplemental services, e.g. displaying phone caller identification, shopping application
- H04N21/4788—Supplemental services, e.g. displaying phone caller identification, shopping application communicating with other users, e.g. chatting
Abstract
The embodiment of the disclosure discloses an information processing method, an information processing device and electronic equipment. The method comprises the following steps: acquiring historical behavior data of a user corresponding to a preset live broadcast room; determining function recommendation information to be displayed based on the historical behavior data, wherein the function recommendation information comprises a recommendation function and a display opportunity corresponding to the recommendation function; and sending the function recommendation information to indicate terminal equipment to display the recommendation function in a display interface of the live broadcast room when the display opportunity arrives. The displayed recommending function can meet the current requirements of the user, and the display time is closer to the time to be executed of the user. When the user has related requirements, the corresponding recommendation function can be quickly realized, and the probability of disturbing the user is reduced.
Description
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to an information processing method and apparatus, and an electronic device.
Background
The network live broadcast enables information transfer to be rapid and convenient, and the special characteristic of strong communication timeliness provides a brand-new communication mode for the majority of users. In the live broadcast process, the anchor can show commodity information, talent information and the like to the user in the live broadcast room through the live broadcast application program, and the watching user can watch the showing content of the anchor. When the viewing user views the presentation content, the viewing user may perform operations such as gift sending, attention and the like in the current scene.
Disclosure of Invention
This disclosure is provided to introduce concepts in a simplified form that are further described below in the detailed description. This disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The embodiment of the disclosure provides an information processing method and device and electronic equipment. The displayed recommending function can meet the current requirements of the user, and the display time is closer to the time to be executed of the user. When the user has related requirements, the corresponding recommendation function can be quickly realized, and the probability of disturbing the user is reduced.
In a first aspect, an embodiment of the present disclosure provides an information processing method, where the method includes: acquiring historical behavior data of a user corresponding to a preset live broadcast room; determining function recommendation information to be displayed based on the historical behavior data, wherein the function recommendation information comprises a recommendation function and a display opportunity corresponding to the recommendation function; and sending the function recommendation information to indicate terminal equipment to display the recommendation function in a display interface of the live broadcast room when the display opportunity arrives.
In a second aspect, an embodiment of the present disclosure provides an information processing apparatus, including: the acquisition module is used for acquiring historical behavior data of a user corresponding to a preset live broadcast room; the determining module is used for determining function recommendation information to be displayed based on the historical behavior data, and the function recommendation information comprises a recommendation function and a display opportunity corresponding to the recommendation function; and the sending module is used for sending the function recommendation information so as to indicate the terminal equipment to display the recommendation function in a display interface of the live broadcast room when the display opportunity arrives.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the information processing method of the first aspect.
In a fourth aspect, the disclosed embodiments provide a computer-readable medium, on which a computer program is stored, which when executed by a processor implements the steps of the information processing method described in the first aspect above.
According to the information processing method, the information processing device and the electronic equipment, historical behavior data of a user corresponding to a preset live broadcast room are obtained firstly; then determining function recommendation information to be displayed based on the historical behavior data, wherein the function recommendation information comprises a recommendation function and a display opportunity corresponding to the recommendation function; and finally, sending the function recommendation information to indicate terminal equipment to display the recommendation function in a display interface of the live broadcast room when the display opportunity arrives. The displayed recommending function can meet the current requirements of the user, and the display time is closer to the time to be executed of the user. When the user has related requirements, the corresponding recommendation function can be quickly realized, and the probability of disturbing the user is reduced.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
FIG. 1 is a flow diagram of one embodiment of an information processing method according to the present disclosure;
FIG. 2 is a schematic block diagram of one embodiment of an information processing apparatus according to the present disclosure;
FIG. 3 is an exemplary system architecture to which the information processing method of one embodiment of the present disclosure may be applied;
fig. 4 is a schematic diagram of a basic structure of an electronic device provided according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, which shows a flowchart of one embodiment of an information processing method according to the present disclosure, as shown in fig. 1, the information processing method includes the following steps 101 to 103.
The preset live broadcast room can comprise a preset room for displaying the information content in a live broadcast mode. The room here may include a space in which the anchor user is located in the preset live room. The anchor user in the preset live broadcast room can display the corresponding information content to the watching user in the live broadcast room. The information content here may be, for example, content related to product sales, content related to teaching, or the like. The anchor user can establish the live broadcast room of own, and after the live broadcast room is established, the watching user can enter the live broadcast room by clicking or long pressing a button which represents that the watching user enters the live broadcast room and the like so as to watch the information content displayed by the anchor user. Here, the viewing user may be regarded as a user corresponding to a preset live broadcast room. In some application scenes, each live broadcast room can have a corresponding room number, and when a preset live broadcast room is set, different live broadcast rooms can be distinguished by setting the corresponding room number.
The historical behavior data may include behaviors performed by the user in a preset live broadcast room before the current time. The historical behavior data may be, for example, comment data that has been executed in the live broadcast room to comment on the content presented by the anchor user, comment data for the anchor user, and the like.
In some application scenarios, after a user performs, for example, comment behaviors, praise behaviors, and the like, behavior data corresponding to the behaviors may be stored. Therefore, historical behavior data corresponding to the preset live broadcast room can be searched in the stored behavior data to obtain the behavior data.
And 102, determining function recommendation information to be displayed based on historical behavior data, wherein the function recommendation information comprises a recommendation function and a display opportunity corresponding to the recommendation function.
The recommendation function may include a function that the user can implement in the live broadcast. For example, an attention function that pays attention to a main broadcasting in the live broadcasting room may be recommended to the user so that the user realizes the attention function by performing an attention operation; the focus operation may be performed by the user clicking a highlighted corresponding button representing the focus anchor, for example. For example, a gift-offering function of offering a gift to a main broadcast in the live broadcast room may also be recommended to the user so that the user implements the gift-offering function by performing a gift-offering operation. The present gift giving operation may be implemented, for example, by a user activating a button that characterizes the gift giving to the main broadcaster.
In the process of implementing the recommendation function, a presentation timing for presenting the recommendation function may be determined. For example, by analyzing the historical behavior data of the user a, if the analysis result indicates that the time for the user a to watch the live game is longer than 3 minutes in the historical behavior data of the user a, the user a will pay attention to the corresponding anchor game. At this time, the attention function may be recommended to the user a. The focus function may be regarded as the above-mentioned recommendation function, and the timing at which the recommendation function can be presented to the user a by 3 minutes from the viewing time of the user a may be regarded as the above-mentioned presentation timing.
In some application scenarios, the function recommendation information to be presented may be determined by analyzing historical behavior data to determine the occurrence probability of a certain behavior at the current time. For example, it may be analyzed that the probability of occurrence of the operation performed by the user B focusing attention on the corresponding tape carrier when viewing the stock live broadcast is the highest compared with other time points if the analysis result indicates that the user B performs the operation focusing attention on the corresponding tape carrier when viewing the stock live broadcast for 10 minutes. The focus function may also be regarded as the recommendation function, and the time when the recommendation function is shown to the user B from the viewing time of the user B to the time of 10 minutes may be regarded as the showing time.
103, sending function recommendation information to indicate the terminal equipment to display the recommendation function in the display interface of the live broadcast room when the display opportunity arrives
The information processing method may be executed by the server, and after the server determines the function recommendation information, the function recommendation information may be sent to the corresponding terminal device. After receiving the recommendation function, the terminal device may determine the recommendation function and a display time corresponding to the recommendation function. And when the display time arrives, the corresponding recommendation function can be displayed to the user.
For example, after the user C enters the makeup live broadcast room, the server may obtain historical behavior data of the user C to determine corresponding function recommendation information. If the determined recommending function is to recommend the purchasing link of the beauty product currently used by the anchor to the user C, the display timing is when 10 seconds after the user C delivers the gift to the anchor arrives. After the terminal device receives the function recommendation information, the recommendation function can be displayed in a display interface of the live broadcast room when the 10 th second from the time when the user C sends the gift to the anchor in the live broadcast room arrives.
When the user sees the corresponding recommendation function in the display interface, if the recommendation function is the function corresponding to the operation that the user wants to execute at the current moment, the corresponding recommendation function can be realized according to the trigger point corresponding to the trigger of the recommendation function.
In some application scenarios, the terminal device may guide the user to execute the recommendation operation through an arrow in a display interface of the live broadcast room, and may attach corresponding recommendation text. For example, when the user is guided to perform the focus operation to realize the corresponding focus function, the trigger point corresponding to the focus operation may be pointed by an arrow, and a wording such as "trigger the anchor which can focus here" may be attached. The user can intuitively know the corresponding recommendation function through the arrow direction and the recommendation text. The arrow here may be set as a colored arrow such as pink, red, etc. The recommended words can be attached to arrows, and can also be displayed through other carriers such as word boxes.
In the prior art, when recommending a function to a user, the function is mostly implemented by guiding the user to execute a corresponding operation in a fixed manner at a fixed time, but when presenting function recommendation information, if a recommendation function that the user currently wants to implement is not presented at a time when the user wants to execute, the user may be disturbed, and then user experience may be reduced.
In the embodiment, historical behavior data of a user corresponding to a preset live broadcast room is obtained firstly; then determining function recommendation information to be displayed based on historical behavior data, wherein the function recommendation information comprises a recommendation function and a display time corresponding to the recommendation function; and finally, sending function recommendation information to indicate the terminal equipment to display the recommendation function in a display interface of the live broadcast room when the display time arrives. The displayed recommending function can meet the current requirements of the user, and the display time is closer to the time to be executed of the user. When the user has related requirements, the corresponding recommendation function can be quickly realized, and the probability of disturbing the user is reduced.
In some application scenarios, a user may not know the related operations that need to be performed to implement the function to be implemented, and may perform other operations that are not related to implementing the function, such as finding the function to be implemented. Since the user performs these redundant operations, the operation cost of the user may be increased. Therefore, the method for enabling the user to directly execute the related operation through the recommendation function provided by the embodiment can reduce the operation cost of the user to a certain extent.
In some optional implementations, the information processing method may further include step 1.
Step 1, determining a user portrait of a user based on historical behavior data of the user, and determining a user category corresponding to the user according to the user portrait;
the user representation may include an identity tag of the user. For example, it may be determined whether the user is a user who likes to watch a game live broadcast (game user), whether the user is a user who likes to watch a makeup live broadcast (makeup user), or the like, according to the historical behavior data of the user. The game user or cosmetic user can be regarded as the user image of the user, namely, an identity tag is given to the user. After the user representation is determined, a corresponding user category may be determined, where the user category of the user may be determined to be a game user category or a cosmetic user category. In some application scenarios, if the user does not have historical behavior data, the user category of the user may be determined to be the first user category corresponding to the new user.
After the user category is determined, step 102 may include step 1021: and determining the function recommendation information to be displayed based on the statistical result of the historical behaviors of the plurality of users belonging to the same user category.
Multiple users may belong to a single user category. For example, user A, user B and user C all like to watch live games, and based on the respective historical behavior data of the three users, it can be determined that their user categories can all be game user categories. In some application scenarios, the function recommendation information to be presented corresponding to the user in the game user category can be determined by counting the historical behavior data of the three users.
That is, the function recommendation information for the current user may be determined according to historical behavior data of other users who belong to the same user category. For example, if a plurality of users in the same user category all concern the anchor after watching the anchor in the live broadcast room for 5 minutes, the corresponding function recommendation information can be displayed in the display interface of the live broadcast room when the current user watches the anchor for 5 minutes, that is, the attention function concerning the anchor is displayed.
In some alternative implementations, the historical behavior data may include first historical behavior interaction sequence data of the user, and step 102 may include step 1022: and determining function recommendation information based on the first historical behavior interaction sequence data.
The first historical behavior interaction sequence data may include a plurality of other related operations that may be performed before an operation is performed. For example, viewing user A typically performs a review action and a like action within 10 seconds prior to a gift delivery. The comment behavior and the like that continuously occur before the current time can be regarded as the first historical behavior interaction sequence data.
After the first historical behavior interaction sequence data is acquired, corresponding function recommendation information can be determined based on the first historical behavior interaction sequence data. For example, according to the first historical behavior interaction sequence data of the viewing user a, the recommendation function corresponding to the viewing user a may be determined to present to the anchor user. The display time corresponding to the recommendation function is within 10 seconds after the watching user A finishes the praise behavior.
In some optional implementations, the information processing method may further include step 2.
Step 2: determining an anchor portrait of the anchor based on historical behavior data of the anchor, and determining an anchor category corresponding to the anchor according to the anchor portrait.
The anchor image here may include the identity tag of the anchor. For example, it may be determined whether the anchor is an anchor of live game contents (game anchor) or an anchor of live makeup contents (makeup anchor) or the like according to the historical behavior data of the anchor. The anchor game or cosmetic anchor herein may be considered an anchor image of the anchor, i.e., an identity tag given to the anchor user. After the anchor representation is determined, a corresponding anchor category may be determined, where the anchor category of the anchor may be determined to be an anchor game category or an anchor make-up category. In some application scenarios, if the anchor does not have historical behavior data, the anchor category of the anchor may be determined to be the first anchor category corresponding to the new anchor.
After the anchor category determination, step 102 may further include step 1023: establishing a first time estimation model according to first historical behavior interaction sequence data of a plurality of anchor in the same anchor category by a plurality of users in the same user category, and determining function recommendation information according to a first output time value of the first time estimation model.
Multiple anchor may belong to one anchor category. For example, anchor A, anchor B, and anchor C can all play live games, and their anchor categories can all be determined to be game anchor categories based on their respective historical behavior data.
In some application scenarios, a first time prediction model may be established by counting first historical behavior interaction sequence data of multiple anchor in the same anchor category for multiple viewing users in the same user category. For example, the first time estimation model may be established by analyzing a first historical behavior interaction sequence of a user A, a user B and a user C in the game user category with respect to a anchor A, an anchor B and an anchor C in the game anchor category. The first time estimation model can estimate the functions which users in the game user category want to realize at a certain time point based on the first historical behavior interaction sequence data. After the first time estimation model estimates the corresponding display opportunity, the information content corresponding to the display opportunity can be output through the first output value. The first output value here may be, for example, information content representing that the attention function is exhibited within 10 seconds of continuously performing the comment behavior and the like.
In some other optional implementation manners, the historical behavior data includes historical behavior transition data of the user in a plurality of scenes, duration data of each historical behavior, and/or a historical parameter value corresponding to a preset income evaluation parameter corresponding to each historical behavior.
The historical behavior transfer data may include historical data of interactive transfers of different behaviors to another behavior, for example, historical data of transfers from a current viewing behavior to a top-up behavior.
The above-mentioned historical behavior duration data may include, for example, historical data of a viewing behavior lasting 30 minutes, historical data of a recharging behavior lasting 1 minute, and the like.
The historical parameter values corresponding to the preset income evaluation parameters corresponding to the historical behaviors can include historical parameter values corresponding to revenues corresponding to the historical behaviors, historical parameter values corresponding to the number of people attracting audiences, and the like.
After obtaining the historical behavior transition data, the duration data of each historical behavior, and/or the historical parameter values corresponding to the preset income evaluation parameters corresponding to each historical behavior of the user in a plurality of scenes, step 102 may include step 1024: and determining function recommendation information based on historical behavior transfer data, historical behavior duration data and/or historical parameter values corresponding to historical behaviors of the user in a plurality of scenes.
For example, if the obtained historical behavior transfer data of the user a in multiple scenes includes that the user a continues to perform the watching behavior after the gift sending behavior lasts for 3 seconds, the watching behavior lasts for 20 minutes, the revenue caused by the gift sending behavior is 5 yuan, and the revenue caused by the watching behavior lasting for 20 minutes is 15 yuan. At this time, it may be determined that the corresponding function recommendation information is to show the viewing function after the user gift sending behavior through the historical behavior data (at this time, the user a may continue to watch without showing other recommendation functions to the user).
In some optional implementations, step 102 may further include step 1025: based on historical behavior transfer data, historical behavior duration data and/or historical parameter values corresponding to historical behaviors of a user in multiple scenes, constructing a behavior estimation model taking the historical behavior duration and weighted values corresponding to the historical parameter values corresponding to the historical behaviors as targets; and determining function recommendation information through the output behavior information of the behavior prediction model.
The behavior estimation model aims to obtain the duration of the historical behavior and the weighted value corresponding to the historical parameter value corresponding to the historical behavior. The weighted value can be calculated by a behavior estimation model. In some application scenarios, when the user performs the output behavior, the historical behavior corresponding to the maximum weighting value may be determined as the output behavior. For example, if the weighted value corresponding to the behavior of the user to purchase the product in the live broadcast room is smaller than the weighted value corresponding to the behavior of the user to watch the live broadcast content, the user may be determined to be the current output behavior without displaying the corresponding recommendation function (product purchase function), and at this time, the function recommendation information determined by the output behavior is the watching function.
In some alternative implementations, the behavior prediction model may be established based on reinforcement learning, and the behavior prediction model may determine the output behavior information based on the following steps one to three.
Step one, counting at least one candidate behavior, and determining the execution probability of each candidate behavior executed by a user and the income generated by executing each candidate behavior.
The method can be used for counting a plurality of candidate behaviors in the current scene in advance, and detecting the execution probability of each candidate behavior and the benefit brought by the execution probability through the behavior estimation model. The profit may include the above-mentioned weighted value, i.e. the weighted value is large, and the profit can be characterized. The candidate behavior may include a top-up behavior, a gift sending behavior, a purchase behavior, etc., if the behavior is in the live broadcast room.
And step two, determining a weighted value corresponding to each candidate behavior according to the execution probability and the preset weight corresponding to each income.
In some application scenarios, preset weights corresponding to the execution probability and the profit may be set. For example, the execution probability may be set to 0.4 and the benefit may be set to 0.6. Thus, after the execution probability and the benefit of the candidate behavior are detected, the corresponding weighted values can be calculated in combination with the respective corresponding weights.
And step three, determining the candidate behavior information corresponding to the weighted value with the maximum numerical value as the output behavior information.
After the weighted value corresponding to each candidate behavior is calculated, comparison may be performed, the candidate behavior with the largest weighted value is determined as the most likely behavior currently executed by the user, and the candidate behavior information is output as the output behavior information.
In some other embodiments, the historical behavior data may include a second historical behavior interaction sequence data of the user within a preset time period before the current time, and step 102 may include step 1026: and establishing a second time estimation model based on the second historical behavior interaction sequence data, and determining function recommendation information through a second output time value of the second time estimation model.
The second historical behavior interaction sequence data may include a plurality of related operations within a preset time period before the current time. For example, the interactive sequence data analysis based on the second historical behavior of the user results in that continuous comment behavior and praise behavior are executed within 3 minutes before the user gives a gift. The second time estimation model established based on the second historical behavior interaction sequence data can estimate that the display timing of the user is within 3 minutes after continuous comment behaviors and praise behaviors. The estimated display timing can be regarded as the information content corresponding to the second output value. The preset time period may include, but is not limited to, 5 minutes before the gift giving, 10 minutes before the gift giving, etc.
In some optional implementation manners, instructing the terminal device to display the recommendation function in the display interface of the live broadcast room when the display opportunity arrives (step 103 above), may include: and indicating the terminal equipment to display the recommendation function in a pop-up window form in a display interface of the live broadcast room when the display opportunity arrives.
That is, after receiving the function recommendation information, the terminal device may present the corresponding recommendation function in a pop-up window form. The recommendation function can be displayed in a pop-up window form to attract the attention of the user, so that the user can quickly execute related operations to realize the corresponding recommendation function.
Referring to fig. 2, which shows a schematic structural diagram of an embodiment of an information processing apparatus according to the present disclosure, as shown in fig. 2, the information processing apparatus includes an obtaining module 201, a determining module 202, and a sending module 203. The acquisition module 201 is configured to acquire historical behavior data of a user corresponding to a preset live broadcast room; the determining module 202 is configured to determine, based on historical behavior data, function recommendation information to be displayed, where the function recommendation information includes a recommendation function and a display opportunity corresponding to the recommendation function; the sending module 203 is configured to send the function recommendation information to instruct the terminal device to display the recommendation function in the display interface of the live broadcast when the display opportunity arrives.
It should be noted that, for specific processing of the obtaining module 201, the determining module 202, and the sending module 203 of the information processing apparatus and technical effects brought by the specific processing, reference may be made to the related descriptions of step 101 to step 103 in the corresponding embodiment of fig. 1, and no further description is given here.
In some optional implementations of this embodiment, the information processing apparatus further includes a user category determination module, where the user category determination module is configured to: determining a user portrait of a user based on historical behavior data of the user, and determining a user category corresponding to the user according to the user portrait; and the determination module 202 is further configured to: and determining the function recommendation information to be displayed based on the statistical result of the historical behaviors of the plurality of users belonging to the same user category.
In some optional implementations of this embodiment, the historical behavior data includes first historical behavior interaction sequence data of the user, and the determination module 202 is further configured to: and determining function recommendation information based on the first historical behavior interaction sequence data.
In some optional implementations of this embodiment, the information processing apparatus further includes an anchor category determination module, where the anchor category determination module is configured to: determining an anchor portrait of the anchor based on historical behavior data of the anchor, and determining an anchor category corresponding to the anchor according to the anchor portrait; and the determination module 202 is further configured to: establishing a first time estimation model according to first historical behavior interaction sequence data of a plurality of anchor in the same anchor category by a plurality of users in the same user category, and determining function recommendation information according to a first output time value of the first time estimation model.
In some optional implementations of the embodiment, the historical behavior data includes historical behavior transition data of the user in a plurality of scenarios, duration data of each historical behavior, and/or a historical parameter value corresponding to a preset benefit assessment parameter corresponding to each historical behavior, and the determining module 202 is further configured to: and determining function recommendation information based on historical behavior transfer data, historical behavior duration data and/or historical parameter values corresponding to historical behaviors of the user in a plurality of scenes.
In some optional implementations of this embodiment, the determining module 202 is further configured to: based on historical behavior transfer data, historical behavior duration data and/or historical parameter values corresponding to historical behaviors of a user in multiple scenes, constructing a behavior estimation model taking the historical behavior duration and weighted values corresponding to the historical parameter values corresponding to the historical behaviors as targets; and determining function recommendation information through the output behavior information of the behavior prediction model.
In some optional implementations of this embodiment, the behavior prediction model determines the output behavior information based on the following steps: counting at least one candidate behavior, and determining the execution probability of each candidate behavior executed by a user and the income generated by executing each candidate behavior; determining a weighted value corresponding to each candidate behavior according to the preset weight corresponding to the execution probability and the income; and determining the candidate behavior information corresponding to the weighted value with the maximum value as the output behavior information.
In some optional implementations of the embodiment, the historical behavior data includes second historical behavior interaction sequence data of the user in a preset time period before the current time, and the determining module 202 is further configured to: and establishing a second time estimation model based on the second historical behavior interaction sequence data, and determining function recommendation information through a second output time value of the second time estimation model.
In some optional implementations of this embodiment, the sending module 203 is further configured to: and indicating the terminal equipment to display the recommendation function in a pop-up window form in a display interface of the live broadcast room when the display opportunity arrives.
Referring to fig. 3, an exemplary system architecture to which the information processing method of one embodiment of the present disclosure may be applied is shown.
As shown in fig. 3, the system architecture may include terminal devices 301, 302, 303, a network 304, and a server 305. The network 304 serves as a medium for providing communication links between the terminal devices 301, 302, 303 and the server 305. Network 304 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few. The terminal devices and servers described above may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., Ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The terminal devices 301, 302, 303 may interact with a server 305 over a network 304 to receive or send messages or the like. The terminal devices 301, 302, 303 may have various client applications installed thereon, such as a video distribution application, a search-type application, and a news-information-type application.
The terminal devices 301, 302, 303 may be hardware or software. When the terminal devices 301, 302, 303 are hardware, they may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like. When the terminal device 301, 302, 303 is software, it can be installed in the electronic devices listed above. It may be implemented as multiple pieces of software or software modules (e.g., software or software modules used to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 305 may be a server that can provide various services, for example, receives an acquisition request of function recommendation information transmitted by the terminal devices 301, 302, 303, performs analysis processing on the acquisition request of function recommendation information, and transmits an analysis processing result (for example, function recommendation information corresponding to the acquisition request) to the terminal devices 301, 302, 303.
It should be noted that the information processing method provided by the embodiment of the present disclosure may be executed by a server, and accordingly, the information processing apparatus may be provided in the server.
It should be understood that the number of terminal devices, networks, and servers in fig. 3 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 4, shown is a schematic diagram of an electronic device (e.g., the server of FIG. 3) suitable for use in implementing embodiments of the present disclosure. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, the electronic device may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 401 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage means 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While fig. 4 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 409, or from the storage device 408, or from the ROM 402. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 401.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring historical behavior data of a user corresponding to a preset live broadcast room; determining function recommendation information to be displayed based on historical behavior data, wherein the function recommendation information comprises a recommendation function and a display opportunity corresponding to the recommendation function; and sending function recommendation information to indicate the terminal equipment to display the recommendation function in a display interface of the live broadcast room when the display opportunity arrives.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The name of the module does not in some cases constitute a limitation on the unit itself, and for example, the acquiring module may also be described as a "module that acquires historical behavior data of a user corresponding to a preset live broadcast room".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (11)
1. An information processing method characterized by comprising:
acquiring historical behavior data of a user corresponding to a preset live broadcast room;
determining function recommendation information to be displayed based on the historical behavior data, wherein the function recommendation information comprises a recommendation function and a display opportunity corresponding to the recommendation function;
sending the function recommendation information to indicate terminal equipment to display the recommendation function in a display interface of the live broadcast room when the display opportunity arrives;
if the historical behavior data includes historical behavior transfer data of the user in a plurality of scenes, duration data of each historical behavior, and/or historical parameter values corresponding to preset income evaluation parameters corresponding to each historical behavior, determining function recommendation information to be displayed based on the historical behavior data includes:
and determining the function recommendation information based on the historical behavior transfer data, the historical behavior duration data and/or the historical parameter values corresponding to the historical behaviors of the user in a plurality of scenes.
2. The method of claim 1, further comprising:
determining a user portrait of the user based on the historical behavior data of the user, and determining a user category corresponding to the user according to the user portrait; and
the determining of the function recommendation information to be displayed based on the historical behavior data includes:
and determining the function recommendation information to be displayed based on the statistical result of the historical behaviors of the plurality of users belonging to the same user category.
3. The method of claim 2, wherein the historical behavior data comprises first historical behavior interaction sequence data of the user, an
The determining of the function recommendation information to be displayed based on the historical behavior data includes:
determining the functional recommendation information based on the first historical behavioral interaction sequence data.
4. The method of claim 3, further comprising:
determining an anchor portrait of an anchor based on historical behavior data of the anchor, and determining an anchor category corresponding to the anchor according to the anchor portrait; and
the determining function recommendation information to be displayed based on the historical behavior data further comprises:
establishing a first time estimation model according to first historical behavior interaction sequence data of a plurality of anchor in the same anchor category of a plurality of users in the same user category, and determining the function recommendation information according to a first output time value of the first time estimation model.
5. The method of claim 1, wherein determining recommendation information for a function to be presented based on the historical behavior data further comprises:
based on the historical behavior transfer data, the historical behavior duration data and/or the historical parameter values corresponding to the historical behaviors of the user in a plurality of scenes, constructing a behavior prediction model taking the historical behavior duration and the weighted values corresponding to the historical parameter values corresponding to the historical behaviors as targets; and
and determining the function recommendation information according to the output behavior information of the behavior prediction model.
6. The method of claim 5, wherein the behavior prediction model determines the output behavior information based on:
counting at least one candidate behavior, and determining the execution probability of each candidate behavior executed by the user and the income generated by executing each candidate behavior;
determining a weighted value corresponding to each candidate behavior according to the execution probability and the preset weight corresponding to the income;
and determining the candidate behavior information corresponding to the weighted value with the largest numerical value as output behavior information.
7. The method of claim 1, wherein the historical behavior data comprises second historical behavior interaction sequence data of the user within a preset time period before a current time, an
The determining of the function recommendation information to be displayed based on the historical behavior data includes:
and establishing a second time estimation model based on the second historical behavior interaction sequence data, and determining the function recommendation information through a second output time value of the second time estimation model.
8. The method according to any one of claims 1 to 7, wherein the instructing the terminal device to present the recommendation function in a presentation interface of the live broadcast room when the presentation opportunity arrives comprises:
and indicating the terminal equipment to display the recommendation function in a display interface of the live broadcast room in a pop-up window mode when the display opportunity arrives.
9. An information processing apparatus characterized by comprising:
the acquisition module is used for acquiring historical behavior data of a user corresponding to a preset live broadcast room;
the determining module is used for determining function recommendation information to be displayed based on the historical behavior data, and the function recommendation information comprises a recommendation function and a display opportunity corresponding to the recommendation function;
the sending module is used for sending the function recommendation information to indicate terminal equipment to display the recommendation function in a display interface of the live broadcast room when the display opportunity arrives;
if the historical behavior data includes historical behavior transition data of the user in a plurality of scenes, duration data of each historical behavior, and/or historical parameter values corresponding to preset income evaluation parameters corresponding to each historical behavior, the determining module is further configured to determine the function recommendation information based on the historical behavior transition data of the user in the plurality of scenes, the duration data of each historical behavior, and/or the historical parameter values corresponding to each historical behavior.
10. An electronic device, comprising:
one or more processors;
storage means having one or more programs stored thereon which, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-8.
11. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-8.
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Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112506406B (en) * | 2020-12-03 | 2021-08-17 | 中国地质大学（北京） | Target control method and system based on user habit self-learning control |
| CN112528151A (en) * | 2020-12-18 | 2021-03-19 | 北京蜜莱坞网络科技有限公司 | Object display method and device, electronic equipment and storage medium |
| CN112632384A (en) * | 2020-12-25 | 2021-04-09 | 北京百度网讯科技有限公司 | Data processing method and device for application program, electronic equipment and medium |
| CN113014934A (en) * | 2021-01-29 | 2021-06-22 | 腾讯科技（深圳）有限公司 | Product display method, product display device, computer equipment and storage medium |
| CN112783984A (en) * | 2021-02-10 | 2021-05-11 | 中国人民银行数字货币研究所 | Information display method and device |
| CN113411627B (en) * | 2021-06-17 | 2023-04-18 | 广州博冠信息科技有限公司 | Data pushing method and device, readable storage medium and electronic equipment |
| CN113408817B (en) * | 2021-07-07 | 2024-04-16 | 北京京东拓先科技有限公司 | Traffic distribution method, device, equipment and storage medium |
| CN113722588B (en) * | 2021-08-12 | 2023-09-05 | 北京达佳互联信息技术有限公司 | Resource recommendation method and device and electronic equipment |
| CN114154071B (en) * | 2021-12-09 | 2023-05-09 | 电子科技大学 | Emotion time sequence recommendation method based on attention mechanism |
| CN115379268B (en) * | 2022-08-05 | 2023-10-03 | 北京达佳互联信息技术有限公司 | Card information display method and device, server, terminal and storage medium |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101677394A (en) * | 2008-09-16 | 2010-03-24 | 中兴通讯股份有限公司 | Method and apparatus for commercial break based on IPTV |
| CN104935970A (en) * | 2015-07-09 | 2015-09-23 | 三星电子（中国）研发中心 | Method for recommending television content and television client |
| CN106028071A (en) * | 2016-05-17 | 2016-10-12 | Tcl集团股份有限公司 | Video recommendation method and system |
| CN107770571A (en) * | 2017-10-26 | 2018-03-06 | 咪咕视讯科技有限公司 | A kind of direct broadcasting room recommends method, apparatus and storage medium |
| CN107820114A (en) * | 2017-09-28 | 2018-03-20 | 武汉斗鱼网络科技有限公司 | Special efficacy methods of exhibiting and device |
| CN108073670A (en) * | 2017-04-12 | 2018-05-25 | 北京市商汤科技开发有限公司 | Business object recommends method, apparatus and electronic equipment |
| CN109525853A (en) * | 2018-11-29 | 2019-03-26 | 北京字节跳动网络技术有限公司 | Direct broadcasting room cover methods of exhibiting, device, terminal, server and readable medium |
| CN109688418A (en) * | 2018-12-24 | 2019-04-26 | 北京潘达互娱科技有限公司 | Interface function bootstrap technique, equipment and storage medium is broadcast live |
| CN109783686A (en) * | 2019-01-21 | 2019-05-21 | 广州虎牙信息科技有限公司 | Behavioral data processing method, device, terminal device and storage medium |
| CN109951724A (en) * | 2017-12-20 | 2019-06-28 | 阿里巴巴集团控股有限公司 | Recommended method, main broadcaster's recommended models training method and relevant device is broadcast live |
| CN110019949A (en) * | 2018-09-27 | 2019-07-16 | 北京字节跳动网络技术有限公司 | Video recommendation method, device, terminal, server and readable medium |
| CN110035314A (en) * | 2019-03-08 | 2019-07-19 | 腾讯科技（深圳）有限公司 | Methods of exhibiting and device, storage medium, the electronic device of information |
| CN110516159A (en) * | 2019-08-30 | 2019-11-29 | 北京字节跳动网络技术有限公司 | A kind of information recommendation method, device, electronic equipment and storage medium |
| CN110896488A (en) * | 2018-08-23 | 2020-03-20 | 武汉斗鱼网络科技有限公司 | Recommendation method for live broadcast room and related equipment |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100262931A1 (en) * | 2009-04-10 | 2010-10-14 | Rovi Technologies Corporation | Systems and methods for searching a media guidance application with multiple perspective views |
| US9817826B2 (en) * | 2012-09-27 | 2017-11-14 | Google Inc. | System and method for recommending media programs and notifying a user before programs start |
| US11287809B2 (en) * | 2016-05-16 | 2022-03-29 | Jabil Inc. | Apparatus, engine, system and method for predictive analytics in a manufacturing system |
-
2020
- 2020-05-18 CN CN202010422378.6A patent/CN111711828B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101677394A (en) * | 2008-09-16 | 2010-03-24 | 中兴通讯股份有限公司 | Method and apparatus for commercial break based on IPTV |
| CN104935970A (en) * | 2015-07-09 | 2015-09-23 | 三星电子（中国）研发中心 | Method for recommending television content and television client |
| CN106028071A (en) * | 2016-05-17 | 2016-10-12 | Tcl集团股份有限公司 | Video recommendation method and system |
| CN108073670A (en) * | 2017-04-12 | 2018-05-25 | 北京市商汤科技开发有限公司 | Business object recommends method, apparatus and electronic equipment |
| CN107820114A (en) * | 2017-09-28 | 2018-03-20 | 武汉斗鱼网络科技有限公司 | Special efficacy methods of exhibiting and device |
| CN107770571A (en) * | 2017-10-26 | 2018-03-06 | 咪咕视讯科技有限公司 | A kind of direct broadcasting room recommends method, apparatus and storage medium |
| CN109951724A (en) * | 2017-12-20 | 2019-06-28 | 阿里巴巴集团控股有限公司 | Recommended method, main broadcaster's recommended models training method and relevant device is broadcast live |
| CN110896488A (en) * | 2018-08-23 | 2020-03-20 | 武汉斗鱼网络科技有限公司 | Recommendation method for live broadcast room and related equipment |
| CN110019949A (en) * | 2018-09-27 | 2019-07-16 | 北京字节跳动网络技术有限公司 | Video recommendation method, device, terminal, server and readable medium |
| CN109525853A (en) * | 2018-11-29 | 2019-03-26 | 北京字节跳动网络技术有限公司 | Direct broadcasting room cover methods of exhibiting, device, terminal, server and readable medium |
| CN109688418A (en) * | 2018-12-24 | 2019-04-26 | 北京潘达互娱科技有限公司 | Interface function bootstrap technique, equipment and storage medium is broadcast live |
| CN109783686A (en) * | 2019-01-21 | 2019-05-21 | 广州虎牙信息科技有限公司 | Behavioral data processing method, device, terminal device and storage medium |
| CN110035314A (en) * | 2019-03-08 | 2019-07-19 | 腾讯科技（深圳）有限公司 | Methods of exhibiting and device, storage medium, the electronic device of information |
| CN110516159A (en) * | 2019-08-30 | 2019-11-29 | 北京字节跳动网络技术有限公司 | A kind of information recommendation method, device, electronic equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| "基于语义和用户偏好的网络电视直播推荐方法";张翔等;《微电子学与计算机》;20161205;全文 * |
| 《CurioView:TV recommendations related to content being viewed》;Hideki Sumiyoshi;《2010 IEEE International Symposium on Broadband Multimdedia Systems and Broadcasting》;20100326;全文 * |
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