CN114302152A

CN114302152A - Live broadcast room recommendation method, device, equipment and storage medium

Info

Publication number: CN114302152A
Application number: CN202111364684.XA
Authority: CN
Inventors: 颜瑞
Original assignee: Beijing Lewo Wuxian Technology Co ltd
Current assignee: Beijing Lewo Wuxian Technology Co ltd
Priority date: 2021-11-17
Filing date: 2021-11-17
Publication date: 2022-04-08

Abstract

The application provides a recommendation method, device, equipment and storage medium for a live broadcast room, relating to the technical field of computers and adopting the following scheme: acquiring account identifiers of N target anchor broadcasters from a historical viewing sequence, wherein the viewing time of users corresponding to the N target anchor broadcasters is later than that of users corresponding to other anchor broadcasters in the historical viewing sequence, and N is a positive integer greater than 1; acquiring N first feature vectors corresponding to account identifiers of N target anchor; determining a similar anchor list corresponding to each target anchor from the online anchors according to the similarity between the N first eigenvectors and the second eigenvectors corresponding to each online anchor respectively; the N similar anchor lists are merged and deduplicated to determine a recommended anchor list. Therefore, the live broadcast room in which the user is interested can be recalled from the online live broadcast room candidate pool with real-time content change according to the historical broadcast watching information, personalized and accurate recommendation for the user is achieved, and the requirement of the user on broadcast watching is met.

Description

Live broadcast room recommendation method, device, equipment and storage medium

Technical Field

The application relates to the technical field of computers, in particular to a recommendation method, device, equipment and storage medium for a live broadcast room.

Background

The personalized recommendation system takes on the responsibility of pushing the live broadcast room in which the user is interested to the user. In order to reduce the amount of calculation and also consider the accuracy of the recommendation result, a modern recommendation system generally needs to recall first, that is, a small amount of materials which may be interested by a user are retrieved from a massive material pool and sent to the next stage for fine sequencing, so that the amount of calculation is reduced.

The personalized recommendation system in the live broadcast software has the characteristic of being different from scenes such as e-commerce, news and the like. In the live broadcasting type software, a live broadcasting room in the live broadcasting software can be frequently on and off-line along with the on and off of a main broadcast, namely the state of the live broadcasting room is changed in real time. Therefore, how to recall the live broadcast room in which the user is interested from the candidate live broadcasts with real-time status changes is a problem that needs to be solved at present.

Disclosure of Invention

The application provides a recommendation method, device and equipment for a live broadcast room and a storage medium.

According to a first aspect of the present application, a recommendation method for a live broadcast room is provided, including:

acquiring account identifiers of N target anchor broadcasters from a historical viewing sequence, wherein the viewing time of users corresponding to the N target anchor broadcasters is later than that of users corresponding to other anchor broadcasters in the historical viewing sequence, and N is a positive integer greater than 1;

acquiring N first feature vectors corresponding to the account identifiers of the N target anchor;

determining a similar anchor list corresponding to each target anchor from the online anchors according to the similarity between the N first eigenvectors and second eigenvectors corresponding to each online anchor respectively;

and merging and de-duplicating the N similar anchor lists to determine a recommended anchor list.

According to a second aspect of the present application, there is provided a recommendation apparatus for a live broadcast room, including:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring account identifiers of N target anchor broadcasters from a historical watching sequence, the watching time of users corresponding to the N target anchor broadcasters is later than that of users corresponding to other anchor broadcasters in the historical watching sequence, and N is a positive integer greater than 1;

a second obtaining module, configured to obtain N first feature vectors corresponding to the account identifiers of the N target anchor;

a first determining module, configured to determine, according to similarities between the N first feature vectors and second feature vectors corresponding to each online anchor, a similar anchor list corresponding to each target anchor from the online anchors;

and the second determining module is used for carrying out merging and deduplication processing on the N similar anchor lists so as to determine a recommended anchor list.

Optionally, the first obtaining module is further configured to:

and acquiring a historical watching sequence corresponding to the current watching account from the distributed cache.

Optionally, the second obtaining module is further configured to:

and inputting each historical watching sequence stored in the distributed cache into a network model generated by pre-training so as to obtain the feature vector corresponding to each anchor, wherein the feature vector of each anchor corresponds to the account identification of the anchor.

Optionally, the first determining module is specifically configured to:

determining a similarity between each of the first feature vectors and each of the second feature vectors;

and determining the online anchor respectively corresponding to the first M second eigenvectors with the highest similarity corresponding to each first eigenvector as the anchor in the similar anchor list corresponding to each target anchor.

Optionally, the first determining module is further configured to:

acquiring account identifications corresponding to all online live broadcast rooms according to a specified period;

and obtaining each online anchor vector corresponding to each account identifier from the distributed cache.

An embodiment of a third aspect of the present application provides a computer device, including: the present invention relates to a computer program product, and a computer program product stored on a memory and executable on a processor, which when executed by the processor performs a method as set forth in an embodiment of the first aspect of the present application.

An embodiment of a fourth aspect of the present application provides a non-transitory computer-readable storage medium storing a computer program, which when executed by a processor implements the method as set forth in the embodiment of the first aspect of the present application.

An embodiment of a fifth aspect of the present application provides a computer program product, which when executed by an instruction processor performs the method provided by the embodiment of the first aspect of the present application.

The recommendation method, device and equipment for the live broadcast room have the following beneficial effects:

in the embodiment of the application, account identifiers of N target anchor are obtained from a historical watching sequence, wherein the watching time of users corresponding to the N target anchor is later than that of users corresponding to other anchor in the historical watching sequence, N is a positive integer larger than 1, N first feature vectors corresponding to the account identifiers of the N target anchor are obtained, then a similar anchor list corresponding to each target anchor is determined from the online anchors according to the similarity between the N first feature vectors and second feature vectors corresponding to each online anchor, and finally the N similar anchor lists are combined and deduplicated to determine a recommended anchor list. Therefore, the live broadcast room in which the user is interested can be recalled from the online live broadcast room candidate pool with real-time content change according to the historical broadcast watching information, personalized and accurate recommendation for the user is achieved, and the requirement of the user on broadcast watching is met.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:

fig. 1 is a schematic flowchart of a recommendation method for a live broadcast room according to the present application;

fig. 2 is a schematic flowchart of another recommendation method for a live broadcast room according to the present application;

fig. 3 is a block diagram of a structure of a recommendation device in a live broadcast room according to the present application.

Fig. 4 is a block diagram of an electronic device for implementing a recommendation method for a live broadcast room according to an embodiment of the present application.

Detailed Description

The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The method for recommending the live broadcast room can be executed by the device for recommending the live broadcast room provided by the application, and also can be executed by the electronic equipment provided by the application, wherein the electronic equipment can include but is not limited to terminal equipment such as a desktop computer and a tablet computer, and the method for recommending the live broadcast room provided by the application is executed by the device for recommending the live broadcast room provided by the application, and is not limited by the application.

A recommendation method for a live broadcast room provided by the present application is described in detail below with reference to the accompanying drawings.

Fig. 1 is a schematic flow chart of a recommendation method for a live broadcast room according to an embodiment of the present application.

As shown in fig. 1, the recommendation method for the live broadcast room may include the following steps:

step 101, acquiring account identifiers of N target anchor broadcasters from a historical viewing sequence, wherein the viewing time of users corresponding to the N target anchor broadcasters is later than the viewing time of users corresponding to other anchor broadcasters in the historical viewing sequence, and N is a positive integer greater than 1.

The historical watch sequence may be a list of account identifications of anchor accounts in a time period of a specified length, for example, it may be a list of IDs of anchors watched by the current user watch account within 90 days.

It should be noted that, in the history viewing sequence, the IDs of the respective anchor may be sorted according to a certain time sequence, for example, according to a time length from the current time, that is, if the viewing time of any anchor is closer to the current time, the position of the anchor in the history viewing sequence is further forward.

The target anchor may be a latest anchor selected currently in the history anchor sequence, that is, the user viewing time corresponding to the target anchor is later than the user viewing times corresponding to the other anchors in the history anchor sequence.

Optionally, the historical viewing sequence corresponding to the current viewing account may be obtained from the distributed cache.

The distributed cache may store the historical viewing sequence of each viewing account, and it can be understood that after the historical viewing sequence of the user is obtained each time, the historical viewing sequence of the user may be stored in the distributed cache. In addition, the user's ID may be stored in association with the historical watch sequence.

And 102, acquiring N first feature vectors corresponding to the account identifications of the N target anchor.

It should be noted that, each historical watch sequence stored in the distributed cache may be first input into a network model generated by pre-training to obtain a feature vector corresponding to each anchor, where the feature vector of each anchor corresponds to an account id of the anchor.

Specifically, the set of historical watchcast sequences stored in the distributed cache may be trained by the item2vec method, so that feature vectors of each anchor in each watchcast sequence may be obtained, for example, a 128-dimensional vector about each anchor may be generated, which is not limited herein.

Alternatively, any vectorization method, such as str2vec, para2vec, etc., may be used, but not limited thereto. It is to be understood that, as another possible implementation manner, the related description information of each anchor may be added to a distributed cache and stored in association with the historical viewing sequence, so that when the generated network model is trained in advance to vectorize each anchor, the generated vector may be enabled to combine the related description information of each anchor, such as age, gender, type, and the like, and is not limited herein.

The first feature vector may be a vector corresponding to each target anchor. Specifically, vectors corresponding to the account id of the first N target anchor may be obtained from the distributed cache, and may be further determined as the first feature vector, which is not limited herein.

And 103, determining a similar anchor list corresponding to each target anchor from the online anchors according to the similarity between the N first feature vectors and the second feature vector corresponding to each online anchor.

The second feature vector may be a vector generated according to related description information of the online anchor, such as an ID, or may further include other valid description information, such as anchor type information, an anchor emotion tag, anchor identity information, and the like, which is not limited herein.

Specifically, the device may determine a difference between the first feature vector and the second feature vector by calculating an inner product of the N first feature vectors and second feature vectors corresponding to all online anchor in the local cache, where a smaller inner product indicates a smaller difference. Finally, M anchor corresponding to the target anchor among the online anchors may be taken as similar anchors, and finally a similar anchor list corresponding to each target anchor may be generated.

Wherein the similar anchor list can be approximated as a list of anchors that are of interest to the current account user.

And 104, merging and removing the duplicate of the N similar anchor lists to determine a recommended anchor list.

In order to finally determine the anchor list of interest recommended for the user, in the application, merging and deduplication processing needs to be performed on the N similar anchor lists, that is, the N similar anchor lists are merged and filtered to obtain a unique recommended anchor list.

Fig. 2 is a schematic flow chart of a recommendation method for a live broadcast room according to an embodiment of the present application.

As shown in fig. 2, the recommendation method for the live broadcast room may include the following steps:

step 201, acquiring account identifiers of N target anchor broadcasts from the historical broadcast watching sequence, wherein the broadcast watching time of users corresponding to the N target anchor broadcasts is later than that of users corresponding to other anchor broadcasts in the historical broadcast watching sequence, and N is a positive integer greater than 1.

Step 202, acquiring N first feature vectors corresponding to account ids of N target anchor.

It should be noted that, for specific implementation manners of

steps

101 and 102, reference may be made to the above embodiments, which are not described herein again.

And 203, acquiring account identifications corresponding to all online live broadcast rooms according to a specified period.

The specified period may be 5 seconds, 8 seconds, 10 seconds, etc., and is not limited herein.

It should be noted that each online live broadcast room corresponds to an account ID, such as an ID or a room number, which is not limited herein. For example, each account id corresponding to each online live broadcast room may be acquired every 5 seconds, which is not limited herein.

And step 204, obtaining each online anchor vector corresponding to each account identifier from the distributed cache.

Specifically, the online anchor vector corresponding to the account identifier of each online live broadcast room may be obtained from the distributed cache according to each account identifier corresponding to each current online live broadcast room, and each online anchor vector may be stored in the local cache.

Step 205, determining the similarity between each first feature vector and each second feature vector.

It should be noted that the similarity between the first feature vector and the second feature vector may be calculated by calculating a cosine similarity between the first feature vector and the second feature vector, or may also be calculated by using an euclidean distance or a manhattan distance formula, which is not limited herein.

Alternatively, an inner vector product between each first feature vector and each second feature vector may be calculated, and if the inner vector product is smaller, the higher the similarity is.

And step 206, determining the online anchor respectively corresponding to the first M second eigenvectors with the highest similarity corresponding to each first eigenvector as the anchor in the similar anchor list corresponding to each target anchor.

Step 207, merging and deduplication processing is performed on the N similar anchor lists to determine a recommended anchor list.

It should be noted that, for specific implementation manners of

steps

206 and 207, reference may be made to the foregoing embodiments, which are not described herein again.

In the embodiment of the application, account identifiers of N target anchor are firstly obtained from a historical watching sequence, wherein the watching time of users corresponding to the N target anchor is later than that of users corresponding to other anchor in the historical watching sequence, N is a positive integer larger than 1, then N first feature vectors corresponding to the account identifiers of the N target anchor are obtained, then each account identifier corresponding to each online live broadcast is obtained according to a specified period, each online anchor vector corresponding to each account identifier is obtained from a distributed cache, the similarity between each first feature vector and each second feature vector is determined, the online anchor corresponding to the first M second feature vectors with the highest similarity corresponding to each first feature vector is determined as the anchor in a similar anchor list corresponding to each target anchor, and finally, carrying out merging and deduplication processing on the N similar anchor lists to determine a recommended anchor list. Therefore, the live broadcast room in which the user is interested can be recalled from the online live broadcast room candidate pool with real-time content change according to the historical broadcast watching information, personalized and accurate recommendation for the user is achieved, and the requirement of the user on broadcast watching is met.

In order to implement the above embodiment, the present application further provides a recommendation device for a live broadcast room.

Fig. 3 is a schematic structural diagram of a recommendation device in a live broadcast room according to an embodiment of the present application.

As shown in fig. 3, the recommendation apparatus 300 for a live broadcast room includes: a first acquisition module 310, a second acquisition module 320, a first determination module 330, and a second determination module 340.

Optionally, the first obtaining module is further configured to:

Optionally, the second obtaining module is further configured to:

Optionally, the first determining module is specifically configured to:

Optionally, the first determining module is further configured to:

FIG. 4 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present application. The computer device 12 shown in fig. 4 is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present application.

As shown in FIG. 4, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. These architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, to name a few.

Computer device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

Memory 28 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 30 and/or cache Memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 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 thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described herein.

Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Moreover, computer device 12 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) via Network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by executing programs stored in the system memory 28.

In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims

1. A recommendation method for a live broadcast room is characterized by comprising the following steps:

2. The method according to claim 1, before obtaining account ids of N target anchor in the historical viewing sequence, further comprising:

3. The method according to claim 1, before the obtaining N first feature vectors corresponding to account ids of the N target anchor, further comprising:

4. The method according to claim 1, wherein said determining a similar anchor list corresponding to each said target anchor from the online anchors according to a similarity between said N first eigenvectors and a second eigenvector corresponding to each online anchor respectively comprises:

5. The method according to claim 1, wherein before determining a similar anchor list corresponding to each of the target anchors from the online anchors according to the similarity between the N first eigenvectors and the second eigenvectors corresponding to each of the online anchors, the method further comprises:

6. A recommendation device for a live broadcast room, comprising:

7. The apparatus of claim 6, wherein the first obtaining module is further configured to:

8. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

9. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.

10. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-5.