CN110730385A - Live broadcast room recommendation method and device, server and storage medium - Google Patents
Live broadcast room recommendation method and device, server and storage medium Download PDFInfo
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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]
- H04N21/20—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
- H04N21/251—Learning process for intelligent management, e.g. learning user preferences for recommending 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
- H04N21/43—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
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
- H04N21/44213—Monitoring of end-user related data
- H04N21/44222—Analytics of user selections, e.g. selection of programs or purchase activity
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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
Abstract
The embodiment of the invention discloses a live broadcast room recommendation method, a live broadcast room recommendation device, a server and a storage medium, wherein the live broadcast room recommendation method comprises the following steps: continuously acquiring and counting behavior data of a target user aiming at each live broadcast room to be recommended in real time; when a recommendation request is received, determining interest scores of all live broadcasting rooms to be recommended according to behavior data counted at the current moment; and sequencing all the live broadcast rooms to be recommended based on the interest scores, and determining a target recommended live broadcast room according to a sequencing result. The embodiment of the invention overcomes the defects that the existing recommendation mode has common effect, and the result set to be recommended is difficult to recalculate in real time according to the user behavior due to poor real-time performance, realizes the effect of rapidly providing a target recommendation live broadcast room for the user in real time by utilizing online data, and improves the accuracy and individuation of recommendation.
Description
Technical Field
The embodiment of the invention relates to the field of internet technology application, in particular to a live broadcast room recommendation method and device, a server and a storage medium.
Background
Many existing applications have a recommendation function, the function is based on historical behavior preference of users, personalized pushing of application contents is carried out on the users, and due to different preference of each user, result sets to be recommended are different.
In the traditional live broadcast field, the recommendation behavior is often analyzed based on offline data of users, and a batch of result sets to be recommended are calculated for each user by counting offline historical behavior data of the users in a database, such as clicking behavior of the users, collection behavior of the users and the like. The effect of the recommendation method is general, and due to poor real-time performance, the result set to be recommended cannot be recalculated in real time according to the user behavior.
Disclosure of Invention
The invention provides a live broadcast room recommendation method, a live broadcast room recommendation device, a server and a storage medium, which are used for rapidly providing a target recommendation live broadcast room for a user in real time and improving the recommendation accuracy and personalization.
In a first aspect, an embodiment of the present invention provides a live broadcast room recommendation method, where the method includes:
continuously acquiring and counting behavior data of a target user aiming at each live broadcast room to be recommended in real time;
when a recommendation request is received, determining interest scores of all live broadcasting rooms to be recommended according to behavior data counted at the current moment;
and sequencing all the live broadcast rooms to be recommended based on the interest scores, and determining a target recommended live broadcast room according to a sequencing result.
In a second aspect, an embodiment of the present invention further provides a live broadcast room recommendation apparatus, where the apparatus includes:
the behavior data storage module is used for continuously acquiring and counting the behavior data of the target user aiming at each direct broadcasting to be recommended in real time;
the interest score determining module is used for determining the interest score of each live broadcast room to be recommended according to the statistical behavior data at the current moment when the recommendation request is received;
and the target recommendation live broadcast room determining module is used for sequencing all the live broadcast rooms to be recommended based on the interest scores and determining the target recommendation live broadcast room according to a sequencing result.
In a third aspect, an embodiment of the present invention further provides a live broadcast room recommendation server, where the server includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a live space recommendation method as described above.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the live broadcast recommendation method as described above.
According to the live broadcast room recommendation method, device, server and storage medium provided by the embodiment of the invention, the behavior data of the target user for each live broadcast room to be recommended is continuously obtained and counted in real time; when a recommendation request is received, determining interest scores of all live broadcasting rooms to be recommended according to behavior data counted at the current moment; the method and the device have the advantages that various live broadcast rooms to be recommended are sorted based on the interest values, the target recommendation live broadcast room is determined according to the sorting result, the defects that the existing recommendation mode is general in effect, due to poor instantaneity, the result set to be recommended is difficult to recalculate in real time according to user behaviors, the effect of rapidly providing the target recommendation live broadcast room for the user in real time by utilizing online data is achieved, and the recommendation accuracy and personalization are improved.
Drawings
The above and other features and advantages of the present invention will become more apparent to those of ordinary skill in the art by describing in detail exemplary embodiments thereof with reference to the attached drawings, in which:
fig. 1 is a flowchart of a live broadcast recommendation method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a live broadcast recommendation method in the second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a live broadcast room recommendation device in a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a live broadcast recommendation server in a fourth 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 is to be further noted that, for the convenience of description, only a part of the structure relating to the present invention is shown in the drawings, not the whole structure.
Example one
Fig. 1 is a flowchart of a live broadcast room recommendation method according to an embodiment of the present invention, where this embodiment is applicable to a situation where a target live broadcast room interested in a user is recommended in real time based on a live broadcast platform, and the method may be executed by a live broadcast room recommendation device, and the device may be implemented by software or hardware. As shown in fig. 1, the method of the embodiment specifically includes:
and S110, continuously acquiring and counting behavior data of the target user aiming at each live broadcast room to be recommended in real time.
In the live broadcast platform, in order to improve the use experience of a user, a live broadcast room in which the user is interested can be recommended in real time for the user according to the online data of the user or by combining the online data with historical offline data. The online data of the user can be obtained continuously in real time after the user logs in the live broadcast platform.
Specifically, the live broadcast room to be recommended may be a live broadcast room in which a user operation behavior exists after the user logs in the live broadcast platform, or may be each live broadcast room in the live broadcast platform. And when the target user is detected to log in the live broadcast platform, the background server monitors the relevant operation behaviors of the target user on each live broadcast room to be recommended in real time on the live broadcast platform and carries out real-time statistics to obtain the behavior data of the target user for each live broadcast room to be recommended.
Preferably, the behavioural data comprises: and each behavior corresponding to each live broadcast room to be recommended and the statistical frequency corresponding to each behavior.
For example, each behavior corresponding to each live broadcast room to be recommended may be a click behavior, a comment behavior, an attention (collection) behavior, a payment behavior, and the like of the target user on each live broadcast room to be recommended. After the user logs in the live broadcast platform, the background server can count the times of all actions corresponding to all live broadcast rooms to be recommended along with the time, and real-time counting times corresponding to all the actions are obtained. And the various behaviors corresponding to the live broadcast rooms to be recommended are in one-to-one correspondence with the statistical times corresponding to the behaviors, and the statistical times are correspondingly stored in the storage space.
For example, each behavior corresponding to each live broadcast room includes clicking, commenting, paying and paying, within half an hour of logging in the live broadcast room, a user respectively performs clicking, commenting and paying operations on the live broadcast room a, wherein the clicking statistical frequency is 1, the commenting statistical frequency is 5, and the paying statistical frequency is 1, and if the storage form of the behavior data of the target user in the storage space can be live broadcast room address + behavior + statistical frequency, the storage form of the behavior data in the storage space can be: live broadcast room a address + click +1, live broadcast room a address + comment +5, live broadcast room a address + concern +0, and live broadcast room a address + pay + 1.
And S120, when a recommendation request is received, determining the interest score of each live broadcast room to be recommended according to the statistical behavior data at the current moment.
The recommendation request can be a command for requesting to recommend a live broadcast room, which is sent to a background server by a user through a live broadcast platform, and the recommendation request can be sent to the background server by the user through clicking a button on a recommendation interface in the live broadcast platform, can also be sent to the background server by the user through refreshing operation on the recommendation interface of the live broadcast platform, and can also be sent to the background server when the user returns a value recommendation interface from other interfaces of the live broadcast platform.
In this embodiment, each live broadcast room to be recommended has its corresponding user behavior, and the user behaviors have statistical behavior data at each time. Each action data can reflect the interest preference of the user to each live broadcast room to be recommended, so that preferably, the interest score of each live broadcast room to be recommended can be determined according to the action data counted at the current moment, and the preference of the user to each candidate live broadcast room can be reflected visually through the interest score.
S130, sorting all the live broadcast rooms to be recommended based on the interest scores, and determining a target recommended live broadcast room according to a sorting result.
In the embodiment, the interest scores can visually reflect the preference of the user on each live broadcast room to be candidate, so that the target recommendation live broadcast room can be determined according to the interest scores of the live broadcast rooms to be recommended. Preferably, the interest scores of all the live broadcast rooms to be recommended can be sorted, and the live broadcast room to be recommended, which is sorted more front, is taken as the target live broadcast recommendation room.
According to the live broadcast room recommendation method provided by the embodiment of the invention, the behavior data of the target user for each live broadcast room to be recommended are continuously obtained and counted in real time; when a recommendation request is received, determining interest scores of all live broadcasting rooms to be recommended according to behavior data counted at the current moment; the method and the device have the advantages that various live broadcast rooms to be recommended are sorted based on the interest values, the target recommendation live broadcast room is determined according to the sorting result, the defects that the existing recommendation mode is general in effect, due to poor instantaneity, the result set to be recommended is difficult to recalculate in real time according to user behaviors, the effect of rapidly providing the target recommendation live broadcast room for the user in real time by utilizing online data is achieved, and the recommendation accuracy and the personalization speed are improved.
On the basis of the foregoing embodiments, further, when a recommendation request is received, determining interest scores of live broadcast rooms to be recommended according to behavior data counted at the current time includes:
determining the weight of each behavior according to a preset rule;
and weighting and summing the statistical times of the current moment by using the weight to determine the interest score.
The behavior data of each live broadcast room to be recommended comprises each behavior executed by the user on the live broadcast room to be recommended and the corresponding statistical times of each behavior. Preferably, a weighted value can be set for each action of each live broadcast room to be recommended, wherein each weighted value is used for reflecting the influence degree of each action on the live broadcast room recommendation result in the live broadcast room recommendation process. After setting the weight for each behavior, weighting processing may be performed in a manner of multiplication by using the statistical number of times that the weight corresponds to the behavior at the current time, so as to obtain a weighting processing result corresponding to the behavior. And then, summing up the weighting processing results corresponding to the behaviors to obtain the weight of the corresponding live broadcast room to be recommended.
Illustratively, after receiving the recommendation request, the statistical behavior data of the target user in the storage space includes: live broadcast room a address + click +1, live broadcast room a address + comment +5, live broadcast room a address + concern +0 and live broadcast room a address + pay + 1; live broadcast room b address + click +1, live broadcast room b address + comment +2, live broadcast room b address + concern +1, and live broadcast room b address + pay + 0. The influence degree of each behavior on the live broadcast room recommendation result can be determined according to the life cycle of the product, and then corresponding weight is determined for each behavior. For example, the user comments of each live room are focused on at the current time, and thus the weight of the comment behavior may be set higher than the weights of other behaviors. Assuming that the weight of the comment behavior is 5 and the weights of the other behaviors are 1, the interest score of the live broadcast room a is calculated by using the data as follows: 1 × 1+5 × 5+0 × 1+1 × 1 ═ 27, the interest score for live room b is: 1 × 1+2 × 5+1 × 1+0 × 1 — 12.
It should be noted that, if the amount of clicks of the target recommendation live broadcast room by the user is reduced after the comment behavior is taken as a main factor influencing live broadcast room recommendation, the recommendation result may be changed by preferably changing the weight of each behavior, so that the recommendation result more conforms to the interest of the user.
Example two
Fig. 2 is a preferred example of a live broadcast recommendation method provided on the basis of the foregoing embodiments. As shown in fig. 2, the method of this embodiment specifically includes:
s210, storing behavior data of a target user aiming at each live broadcast room to be recommended in real time by utilizing a Redis storage system, wherein the behavior data comprises: and each behavior corresponding to each live broadcast room to be recommended and the statistical frequency corresponding to each behavior.
In this embodiment, a plurality of storage systems for storing behavior data may be provided, for example, Sql Server, MySql, Redis, and the like. Because the Redis storage system is based on the memory, the real-time performance is good, the transverse expansion is convenient, and the storage capacity is large, the Redis storage system can be preferably used for storing the behavior data in real time.
The Redis storage system can store behavior data of each user logging in the live broadcast platform, and if the number of logged users is too large, distributed expansion can be preferentially carried out on the Redis storage system to increase the storage space of the behavior data. In this embodiment, only the target user is taken as an example for description.
Preferably, a Redis storage system is used for storing each behavior of the target user for each live broadcast room to be recommended and the corresponding statistical times of each behavior in real time. The actions can include actions of clicking, commenting, paying and the like of the user on the live broadcast room, and correspondingly, the statistical times corresponding to the actions can be the total times of clicking, commenting, paying and the like of the user on the live broadcast room at the current moment.
And S220, storing each behavior corresponding to each live broadcast room to be recommended as each keyword key in the Redis storage system.
And S230, storing the statistical times corresponding to the behaviors as the value of each keyword key, wherein the structure type of the value is a ZSTAT type.
Preferably, each behavior corresponding to each live broadcast room to be recommended may be taken as each keyword key in the Redis storage system, the statistical number corresponding to each behavior may be taken as the value of each keyword key in the Redis storage system, and the structure type of the value is ZSET, so as to sequentially record the statistical number corresponding to each behavior.
For example, the behavior data of the target user may be stored in the Redis storage system in the form of target user ID + behavior + live broadcast number + statistics, if the target user ID is 123 and the live broadcast number is 666, each behavior includes click, comment, attention and payment, and the statistics corresponding to each behavior at the current time are 1, 5, 1 and 1, respectively (the statistics are values that change in real time). The behavior data of the target user is stored in the Redis storage system in the following form:
target user 123 clicks 666 room 1 time: ZINCRBY 123_ click 6661;
target user 123 reviews 666 room 1 time: zincbly 123_ comment 6665;
target user 123 pays 1 time at 666 rooms: zincbly 123_ pay 6661;
target user 123 focuses 666 room 1 time: ZINCRBY 123_ follow 6661.
And S240, when a recommendation request is received, determining the interest score of each live broadcast room to be recommended according to the statistical behavior data at the current moment by using a ZUNIONSTORE command in the Redis storage system.
The code of the illustrative ZUNIONSTORE command is as follows:
ZUNIONSTORE 123_recommend 4 123_click 123_comment 123_follow 123_payWEIGHTS 1 5 1 1
the above code can be understood as: the click action key, comment action key, payment action key, and attention action key of the target user 123 are weighted and summed to calculate the interest score. Specifically, when each key has a certain live broadcast, the interest scores are weighted and summed according to 1 time of the total click times, 5 times of the total comment times, 1 time of the total payment times and 1 time of the total attention times, and the weighted and summed interest scores are written into the 123_ recommend in the Redis storage system.
The setting of the weight for each behavior can reflect the influence of the key behavior better and improve the recommendation quality.
And S250, sequencing all the live broadcast rooms to be recommended based on the interest scores, and determining a target recommended live broadcast room according to a sequencing result.
And sequencing according to the interest scores on the basis of the obtained 123_ recommend result, wherein the live broadcast room number corresponding to each sequencing is a matching object for real-time evaluation according to the user interest.
The code of the above process is: zrevrage 123_ recommendation and 09, which code may be understood to target the recommendation of live room to the live room ranked 10 above the interest score of user 123.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a live broadcast room recommendation device in a third embodiment of the present invention. As shown in fig. 3, the live room recommendation apparatus includes:
the behavior data storage module 310 is used for continuously acquiring and counting the behavior data of the target user for each live broadcast room to be recommended in real time;
the interest score determining module 320 is configured to determine, when a recommendation request is received, an interest score of each live broadcast room to be recommended according to behavior data counted at the current time;
and the target recommendation live broadcast room determining module 330 is configured to sort the to-be-recommended live broadcast rooms based on the interest scores, and determine the target recommendation live broadcast room according to a sorting result.
According to the live broadcast room recommending device provided by the embodiment of the invention, the behavior data of the target user for each live broadcast room to be recommended is continuously acquired and counted in real time through the behavior data storage module, when a recommending request is received, the interest score of each live broadcast room to be recommended is determined by the interest score determining module according to the behavior data counted at the current moment, the target recommending live broadcast room determining module is used for sequencing each live broadcast room to be recommended based on the interest score, and the target recommending live broadcast room is determined according to the sequencing result.
On the basis of the above embodiments, further, when the behavior data includes: when each behavior corresponding to each live broadcast room to be recommended and the number of statistics corresponding to each behavior are counted, the interest score determining module 320 may include:
the weight determining unit is used for determining the weight of each behavior according to a preset rule;
and the interest score determining unit is used for weighting and summing all the statistical times of the current moment by using the weights to determine the interest score.
Further, the live broadcast room recommending device may further include:
and the Redis storage module is used for storing behavior data of the target user aiming at each live broadcast room to be recommended in real time by utilizing a Redis storage system.
Further, the Redis memory module may include:
the keyword storage unit is used for storing each behavior corresponding to each live broadcast room to be recommended as each keyword key in the Redis storage system;
and the value storage unit is used for storing the statistical times corresponding to the behaviors as the value of each keyword key, wherein the structure type of the value is a ZSET type.
Further, interest score determination module 320 may be further configured to:
and when a recommendation request is received, determining the interest score of each live broadcast room to be recommended according to the behavior data counted at the current moment by using a ZUNIONSTORE command in the Redis storage system.
The live broadcast room recommendation device provided by the embodiment of the invention can execute the live broadcast room recommendation method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 4 is a schematic structural diagram of a live broadcast room recommendation server provided by the fourth embodiment of the present invention. Fig. 4 illustrates a block diagram of an exemplary live room recommendation server 412 suitable for use in implementing embodiments of the present invention. The live-air recommendation server 412 shown in fig. 4 is only an example, and should not bring any limitations to the function and scope of the embodiments of the present invention.
As shown in fig. 4, the live room recommendation server 412 is in the form of a general purpose computing device. Components of the live room recommendation server 412 may include, but are not limited to: one or more processors 416, a memory 428, and a bus 418 that couples the various system components (including the memory 428 and the processors 416).
The live room recommendation server 412 typically includes a variety of computer system readable media. These media may be any available media that can be accessed by the live room recommendation server 412 and include both volatile and nonvolatile media, removable and non-removable media.
A program/utility 440 having a set (at least one) of program modules 442 may be stored, for instance, in memory 428, such program modules 442 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination may include an implementation of a network environment. The program modules 442 generally perform the functions and/or methodologies of the described embodiments of the invention.
The live room recommendation server 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing device, display 424, etc., where the display 424 may be configurable or not as desired), one or more devices that enable a user to interact with the live room recommendation server 412, and/or any device (e.g., network card, modem, etc.) that enables the live room recommendation server 412 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interfaces 422. Also, the live room recommendation server 412 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) through a network adapter 420. As shown, the network adapter 420 communicates with the other modules of the live room referrer server 412 over a bus 418. It should be appreciated that although not shown in fig. 4, other hardware and/or software modules may be used in conjunction with the live room recommendation server 412, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage, among others.
The processor 416 executes various functional applications and data processing, such as implementing a live-air recommendation method provided by an embodiment of the present invention, by executing programs stored in the memory 428.
EXAMPLE five
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a live broadcast recommendation method provided in an embodiment of the present invention, and the method includes:
continuously acquiring and counting behavior data of a target user aiming at each live broadcast room to be recommended in real time;
when a recommendation request is received, determining interest scores among all direct broadcasts to be recommended according to behavior data counted at the current moment;
and sequencing all the live broadcast rooms to be recommended based on the interest scores, and determining a target recommended live broadcast room according to a sequencing result.
Of course, the computer program stored on the computer-readable storage medium provided in the embodiment of the present invention is not limited to execute the method operations described above, and may also execute related operations in the live broadcast recommendation method based on the live broadcast recommendation server provided in any embodiment of the present invention.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. 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 (a non-exhaustive list) of the computer readable storage medium would include the following: 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 context of this document, 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, 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 wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like 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).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions without departing from the scope of the invention. Therefore, although the present invention has been described in more 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 (10)
1. A live broadcast room recommendation method is characterized by comprising the following steps:
continuously acquiring and counting behavior data of a target user aiming at each live broadcast room to be recommended in real time;
when a recommendation request is received, determining interest scores of all live broadcasting rooms to be recommended according to behavior data counted at the current moment;
and sequencing all the live broadcast rooms to be recommended based on the interest scores, and determining a target recommended live broadcast room according to a sequencing result.
2. The method of claim 1, wherein the behavior data comprises:
and each behavior corresponding to each live broadcast room to be recommended and the statistical frequency corresponding to each behavior.
3. The method according to claim 2, wherein when a recommendation request is received, determining the interest score of each live broadcast room to be recommended according to the behavior data counted at the current moment comprises:
determining the weight of each behavior according to a preset rule;
and weighting and summing the statistical times of the current moment by using the weight to determine the interest score.
4. The method of claim 2, further comprising:
and storing behavior data of the target user aiming at each live broadcast room to be recommended in real time by using a Redis storage system.
5. The method according to claim 4, wherein the storing behavior data of the target user for each live broadcast room to be recommended in real time by using a Redis storage system comprises:
storing each behavior corresponding to each live broadcast room to be recommended as each keyword key in the Redis storage system;
and storing the statistical times corresponding to the behaviors as the value of each keyword key, wherein the structure type of the value is the ZSTAT type.
6. The method according to claim 5, wherein when a recommendation request is received, determining the interest score of each live broadcast room to be recommended according to the behavior data counted at the current moment comprises:
and when a recommendation request is received, determining the interest score of each live broadcast room to be recommended according to the behavior data counted at the current moment by using a ZUNIONSTORE command in the Redis storage system.
7. The method of any of claims 1-6, wherein the request for recommendation comprises:
refreshing the recommendation interface or returning to the recommendation interface by other interfaces.
8. A live room recommendation device, comprising:
the behavior data storage module is used for continuously acquiring and counting the behavior data of the target user aiming at each live broadcast room to be recommended in real time;
the interest score determining module is used for determining the interest score of each live broadcast room to be recommended according to the behavior data counted at the current moment when the recommendation request is received;
and the target recommendation live broadcast room determining module is used for sequencing all the live broadcast rooms to be recommended based on the interest scores and determining the target recommendation live broadcast room according to a sequencing result.
9. A live room recommendation server, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a live-air recommendation method as recited in any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a live-room recommendation method according to any one of claims 1-7.
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