CN114154013A - Video recommendation method, device, equipment and storage medium - Google Patents
Video recommendation method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN114154013A CN114154013A CN202111250121.8A CN202111250121A CN114154013A CN 114154013 A CN114154013 A CN 114154013A CN 202111250121 A CN202111250121 A CN 202111250121A CN 114154013 A CN114154013 A CN 114154013A
- Authority
- CN
- China
- Prior art keywords
- video
- candidate
- videos
- user
- video set
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/73—Querying
- G06F16/735—Filtering based on additional data, e.g. user or group profiles
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/75—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Computational Linguistics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
Abstract
The embodiment of the disclosure provides a video recommendation method, a video recommendation device, video recommendation equipment and a storage medium. The method comprises the steps of obtaining a candidate video set matched with a user according to a video recommendation request of the user; adjusting the display sequence of the candidate videos in the candidate video set according to the types of the candidate videos to obtain a recommended video set; and recommending videos to users according to the recommended video set. In this way, video recommendation can be accurately and effectively carried out on the user, and meanwhile, the problem that videos of other interested categories of users cannot be provided for the user or can only be presented in a subsequent page to increase browsing and searching time of the user due to the fact that videos of the same category are recommended to the user in a concentrated mode is avoided. And can be updated in time according to user operation, further improve the variety of video recommendation.
Description
Technical Field
The present disclosure relates to the field of data processing, and more particularly, to the field of video recommendation technology.
Background
With the continuous development of internet video technology, more and more users watch videos through a video platform.
The recommendation technology of the current video needs to establish a recommendation model in advance, the recommendation technology based on the recommendation model is mainly suitable for scenes with sufficient user quantity and resources, and when the resource quantity, the user quantity and historical data included in a video platform are less, a complete recommendation model is difficult to establish. For example, videos recommended to a user may be concentrated on one or a few types or related content, such as a system that automatically and continuously recommends entertainment programs after the user watches the entertainment programs, or a system that continuously recommends a plurality of entertainment programs and continuously recommends a plurality of movie programs; the actual needs of the user are not well met.
Disclosure of Invention
The disclosure provides a method, an apparatus, a device and a storage medium for video recommendation.
According to a first aspect of the present disclosure, a video recommendation method is provided. The method comprises the following steps: acquiring a candidate video set matched with a user according to a video recommendation request of the user; adjusting the display sequence of the candidate videos in the candidate video set according to the types of the candidate videos to obtain a recommended video set; and recommending videos to users according to the recommended video set.
In some embodiments, obtaining a candidate video set matching a user according to a video recommendation request of the user includes:
clustering users according to video tags of historical videos browsed by the historical users, and determining user types and video tags corresponding to the types; then, sorting is carried out according to the relevance scores of the video tags of the videos in the video library and the video tags corresponding to the user types, and N videos which are sorted in the front are used as candidate videos; or,
acquiring related videos from a video library according to keywords included in the video recommendation request, sorting the videos according to the relevance between the labels of the videos and the keywords, and taking N videos which are sorted at the top as candidate videos to generate a candidate video set;
wherein N is a positive integer greater than 1.
In some embodiments, performing presentation order adjustment on the candidate videos in the candidate video set according to the categories of the candidate videos includes:
determining the display sequence of the video categories according to the category sequence of N candidate videos in the candidate video set;
sequentially presenting one or more videos of each category;
then one or more videos in the remaining videos of each category are displayed in sequence according to the display sequence of the video categories;
until all the candidate videos are displayed.
In some embodiments, the method further comprises:
and determining a corresponding default video category sorting rule according to the user type of the user, and adjusting the display sequence of the determined video categories.
In some embodiments, the method further comprises:
and adjusting the default video category sorting rule according to the video operation behaviors of the candidate videos of different categories in the historical behaviors of the user.
In some embodiments, performing presentation order adjustment on the candidate videos in the candidate video set according to the categories of the candidate videos includes:
maintaining a cooling list, and recording videos and video types which have been recommended to users;
sequentially inserting candidate videos in a candidate video set into the cooling list;
judging whether a current candidate video in a candidate video set meets an insertion condition, wherein the insertion condition is that the minimum position interval between the current candidate video and a video of the corresponding category in a cooling list is larger than a preset threshold value, and if the insertion condition is met, inserting the current candidate video into the cooling list; if the insertion condition is not met, judging whether the subsequent candidate videos of the current candidate video meet the insertion condition or not until one candidate video in the subsequent candidate videos of the current candidate video meets the insertion condition and is inserted into the cooling list, and judging whether the current candidate video and the candidate video which is inserted into the cooling list and is before the candidate video meeting the insertion condition or not until the candidate video in the candidate video set is inserted into the cooling list.
According to a second aspect of the present disclosure, a video recommendation apparatus is provided. The device includes: the candidate video set acquisition module is used for acquiring a candidate video set matched with the user according to the video recommendation request of the user; a recommended video set generation module; the display order adjustment is carried out on the candidate videos in the candidate video set according to the types of the candidate videos to obtain a recommended video set; and the recommending module is used for recommending videos to the user according to the recommended video set.
According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory having a computer program stored thereon and a processor implementing the method as described above when executing the program.
According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as according to the first and/or second aspects of the present disclosure.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
By the video recommendation method, the videos can be accurately and effectively recommended to the user, the problem that videos of the same category which are interesting to other users cannot be provided for the user or can only be presented in the subsequent pages, the browsing and searching time of the user is increased due to the fact that videos of other categories are recommended to the user in a concentrated mode is avoided, updating can be carried out in time according to user operation, and the diversity of video recommendation is further improved.
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. The accompanying drawings are included to provide a further understanding of the present disclosure, and are not intended to limit the disclosure thereto, and the same or similar reference numerals will be used to indicate the same or similar elements, where:
fig. 1 shows a flow diagram of a video recommendation method according to an embodiment of the present disclosure;
FIG. 2 shows a block diagram of a video recommendation device according to an embodiment of the present disclosure;
FIG. 3 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Fig. 1 shows a flow diagram of a video recommendation method 100 according to an embodiment of the disclosure.
At block 105, according to the video recommendation request of the user, obtaining a candidate video set matched with the user;
in some embodiments, the user is a user viewing a video using a client provided by the video platform, and the user may be a user having a personal account on the video platform or a user viewing the video as a visitor.
In some embodiments, candidate videos matching the user are obtained according to the video recommendation request of the user. The video recommendation request can be input by a user in a search box of a client; or the user clicks or long-presses a set recommendation button on the client to trigger generation; the generation may also be triggered after the user performs a set user operation on the client (for example, the screen-sliding browsing time exceeds a set threshold), which is not limited in this embodiment. The video recommendation request may also be automatically generated by the client after the user logs in the client.
In some embodiments, the type of historical video that the current user has visited is determined according to the historical behavior of the current user; a plurality of candidate videos is determined according to the historical video types. The historical behavior of the current user may include a video viewing record, a video search record, etc. of the current user.
In some embodiments, a plurality of candidate videos are determined as a candidate video set according to historical video types; clustering users according to video tags of historical videos browsed by historical users, and determining user types and video tags corresponding to the types; and then, sorting according to the relevance scores of the video tags of the videos in the video library and the video tags corresponding to the user types, and taking N videos which are sorted at the top as candidate videos, wherein N is a positive integer larger than 1. Wherein the video tag is determined according to a plurality of the title, image content, text content, user annotation information and video type of the video, and video author category. In some embodiments, the user type may also be determined by personal information filled in during the user registration process, a tag, or a user type selected by the user.
In some embodiments, keyword recognition is performed on a title of a video to obtain a title keyword; carrying out image content identification on the video to obtain an entity; performing text recognition on the video to obtain text keywords; determining a user labeling label according to the user labeling information of the video; and determining the label of the video according to the title key words, the entity, the text key words, the user labeling label and a plurality of video classifications.
In some embodiments, according to a video recommendation request of a user, candidate videos matched with the user are obtained and serve as a candidate video set; the method comprises the steps of obtaining related videos from a video library according to keywords included in the video recommendation request, sequencing according to the relevance between the labels of the videos and the keywords, and taking N videos which are sequenced at the top as candidate videos to generate a candidate video set. In some embodiments, related videos are obtained from a video library and ranked according to the relevance between the description information of the videos and the keywords, and the top N videos are taken as candidate videos, wherein N is a positive integer greater than 1.
At block 110, adjusting the candidate videos in the candidate video set to obtain a recommended video set;
in some embodiments, the candidate video set is obtained according to the relevance between the tags of the videos and the keywords included in the user type and/or the video recommendation request of the user, so that it is highly likely that the videos recommended to the user are concentrated in one or a few types or relevant contents, such as continuously recommending entertainment programs, or continuously recommending a plurality of movie programs after continuously recommending a plurality of entertainment programs; therefore, the ordering of the candidate videos therein needs to be adjusted.
In some embodiments, the N candidate videos in the candidate video set are ranked by their video type; the method comprises the following steps:
determining the display sequence of the video categories according to the category sequence of N candidate videos in the candidate video set; sequentially presenting one or more videos of each category; for example: in the candidate video set, the first video is ranked as a movie, the second video is ranked as a movie, the third video is ranked as a movie, the fourth video is ranked as a movie, the fifth video is ranked as a movie, the sixth video is ranked as a documentary, and the seventh video is ranked as an entertainment program. The determined presentation order of the video categories is: movies, documentaries, entertainment programs.
In some embodiments, the display order of the video categories may be determined according to an order of categories of the first M candidate videos in the candidate video set, where M is a positive integer less than N; then one or more videos of each category are displayed in sequence according to the display sequence of the video categories; and if all the candidate videos are not displayed, sequentially displaying one or more videos of each category in the remaining candidate videos according to the display sequence of the video categories until all the candidate videos are displayed.
Through the operation, all related candidate videos are recommended to the user, uniform recommendation of the videos in different categories is achieved, the situation that the same type of candidate videos are recommended to the user in a concentrated mode, other types of candidate videos cannot be displayed to the user in time, and the user is likely to need to turn down continuously to obtain the interested candidate videos of the video categories can be avoided.
In some embodiments, a corresponding default video category sorting rule is determined according to a user type of a user, and a display order of the determined video categories is adjusted. The method and the device can avoid the problem that video recommendation cannot be accurately and effectively carried out on the user under the condition that the historical behavior data of the user is insufficient. According to the default video category sorting rule corresponding to the user type, the preference of the user of the same type as the user can be obtained, and the video category sorting can be better adjusted, not only according to the original category sorting mode of the candidate videos in the candidate video set.
In some embodiments, according to the historical behaviors of the user, video operation behaviors of different types of candidate videos, such as praise, collection or repeated watching of the candidate videos by the user, can be performed, the tendency of the user to watch the video categories of the videos can be determined, the video categories are scored, the default video category sorting rule is adjusted according to the scoring result, and then the corresponding video category sorting rule can be set for the user, so that the videos can be accurately and effectively recommended to the user.
In some embodiments, the ranking order of the categories in the candidate video set is adjusted according to the user's propensity to watch the video type of the video, the ranking position of the video category with higher score is advanced, and/or the number of candidate videos for the category is increased.
In some embodiments, the N candidate videos in the candidate video set are ranked by their video type; the method comprises the following steps:
maintaining a cooling list, and recording videos and video types which have been recommended to users; sequentially inserting candidate videos in a candidate video set into the cooling list; judging whether a current candidate video in a candidate video set meets an insertion condition, wherein the insertion condition is that the minimum position interval between the current candidate video and a video of the corresponding category in a cooling list is larger than a preset threshold value, and if the insertion condition is met, inserting the current candidate video into the cooling list; if the insertion condition is not met, judging whether the subsequent candidate videos of the current candidate video meet the insertion condition or not until one candidate video in the subsequent candidate videos of the current candidate video meets the insertion condition and is inserted into the cooling list, and judging whether the current candidate video and the candidate video which is inserted into the cooling list and is before the candidate video meeting the insertion condition or not until the candidate video in the candidate video set is inserted into the cooling list. For example: the first video is sequenced into a movie, the second video is sequenced into a movie, the third video is sequenced into a movie, the fourth video is sequenced into a movie, the fifth video is sequenced into a movie, the sixth video is sequenced into a documentary, and the seventh video is sequenced into an entertainment program; firstly, inserting a first-ordered video into the cooling list, judging the forward movement of a third-ordered video if the interval of a second-ordered video is not equal to a preset interval because the category of the second-ordered video is the same as the category of the first-ordered video in the cooling list, and inserting the sixth-ordered video into the cooling list if the category of the third-ordered video is different from the category of the sixth-ordered video; and then judging whether the interval of the positions of the candidate videos of the category in the second ordered video and the cooling list thereof reaches a preset interval, wherein the sixth ordered video is separated from the first ordered video and can be inserted into the cooling list. Through the operation, the situation that the candidate videos of the same type are recommended to the user in a concentrated mode, the candidate videos of other types cannot be displayed to the user in time, and the user is likely to need to turn down continuously to obtain the candidate videos of the video category in which the user is interested.
In some embodiments, N candidate videos in the candidate video set are first deduplicated before their ranking is adjusted according to their video type.
In the process of refreshing the recommended video for N times (N is an integer greater than 1) by the user, recommending no repeated video to the user, namely, removing the repeated video according to the display time factor; alternatively, within a set time period (e.g., 5 minutes, or 10 minutes, etc.) after a certain video is recommended to the user, the video is not recommended to the user.
At block 115, a video recommendation is made to the user based on the recommended video set.
In some embodiments, video recommendation is performed to the user according to the recommended video set, including presenting candidate videos in the recommended video set to the user according to a preset rule in a list form or a loop form.
In some embodiments, the method further comprises: and acquiring video operation behaviors of the user on different types of candidate videos according to the target, such as praise, collection or repeated watching of the candidate videos by the user, wherein the behaviors can determine the tendency of the user to watch the video types of the videos, score the video types, and adjust a default preset rule according to a scoring result.
According to the embodiment of the disclosure, the following technical effects are achieved:
the method and the device can accurately and effectively recommend videos to the user, and simultaneously avoid the problem that videos of other interesting categories of the user cannot be provided for the user or can only be presented in a subsequent page to increase the browsing and searching time of the user because the videos of the same category are recommended to the user in a concentrated manner. And the video recommendation method can be updated in time according to user operation, so that the pertinence of video recommendation is further improved.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
Fig. 2 shows a block diagram of a video recommendation device 200 according to an embodiment of the present disclosure. As shown in fig. 2, the apparatus 200 includes:
the candidate video set obtaining module 202 is configured to obtain a candidate video set matched with a user according to a video recommendation request of the user;
a recommended video set generating module 204; the display order adjustment is carried out on the candidate videos in the candidate video set according to the types of the candidate videos to obtain a recommended video set;
and the recommending module 206 is configured to recommend videos to the user according to the recommended video set.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 3 shows a schematic block diagram of an electronic device 300 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
The device 300 comprises a computing unit 301 which may perform various suitable actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)302 or a computer program loaded from a storage unit 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The calculation unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Various components in device 300 are connected to I/O interface 305, including: an input unit 306 such as a keyboard, a mouse, or the like; an output unit 307 such as various types of displays, speakers, and the like; a storage unit 308 such as a magnetic disk, optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 301 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 301 performs the various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 300 via ROM 302 and/or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the method 100 by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
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.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (9)
1. A video recommendation method, comprising:
acquiring a candidate video set matched with a user according to a video recommendation request of the user;
adjusting the display sequence of the candidate videos in the candidate video set according to the types of the candidate videos to obtain a recommended video set;
and recommending videos to users according to the recommended video set.
2. The method according to claim 1, wherein a candidate video set matching the user is obtained according to the video recommendation request of the user; the method comprises the following steps:
clustering users according to video tags of historical videos browsed by the historical users, and determining user types and video tags corresponding to the types; then, sorting is carried out according to the relevance scores of the video tags of the videos in the video library and the video tags corresponding to the user types, and N videos which are sorted in the front are used as candidate videos; or,
acquiring related videos from a video library according to keywords included in the video recommendation request, sorting the videos according to the relevance between the labels of the videos and the keywords, and taking N videos which are sorted at the top as candidate videos to generate a candidate video set;
wherein N is a positive integer greater than 1.
3. The method according to claim 2, wherein the candidate videos in the candidate video set are adjusted in display order according to the categories of the candidate videos; the method comprises the following steps:
determining the display sequence of the video categories according to the category sequence of N candidate videos in the candidate video set;
sequentially presenting one or more videos of each category;
then one or more videos in the remaining videos of each category are displayed in sequence according to the display sequence of the video categories;
until all the candidate videos are displayed.
4. The method of claim 3, wherein the method further comprises:
and determining a corresponding default video category sorting rule according to the user type of the user, and adjusting the display sequence of the determined video categories.
5. The method of claim 4, wherein the method further comprises:
and adjusting the default video category sorting rule according to the video operation behaviors of the candidate videos of different categories in the historical behaviors of the user.
6. The method of claim 2, wherein the adjusting of the presentation order of the candidate videos in the candidate video set according to the category of the candidate videos comprises:
maintaining a cooling list, and recording videos and video types which have been recommended to users;
sequentially inserting candidate videos in a candidate video set into the cooling list;
judging whether a current candidate video in a candidate video set meets an insertion condition, wherein the insertion condition is that the minimum position interval between the current candidate video and a video of the corresponding category in a cooling list is larger than a preset threshold value, and if the insertion condition is met, inserting the current candidate video into the cooling list; if the insertion condition is not met, judging whether the subsequent candidate videos of the current candidate video meet the insertion condition or not until one candidate video in the subsequent candidate videos of the current candidate video meets the insertion condition and is inserted into the cooling list, and judging whether the current candidate video and the candidate video which is inserted into the cooling list and is before the candidate video meeting the insertion condition or not until the candidate video in the candidate video set is inserted into the cooling list.
7. A video recommendation apparatus comprising:
the candidate video set acquisition module is used for acquiring a candidate video set matched with the user according to the video recommendation request of the user;
a recommended video set generation module; the display order adjustment is carried out on the candidate videos in the candidate video set according to the types of the candidate videos to obtain a recommended video set;
and the recommending module is used for recommending videos to the user according to the recommended video set.
8. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
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 1-6.
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 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111250121.8A CN114154013A (en) | 2021-10-26 | 2021-10-26 | Video recommendation method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111250121.8A CN114154013A (en) | 2021-10-26 | 2021-10-26 | Video recommendation method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114154013A true CN114154013A (en) | 2022-03-08 |
Family
ID=80458221
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111250121.8A Pending CN114154013A (en) | 2021-10-26 | 2021-10-26 | Video recommendation method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114154013A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114615525A (en) * | 2022-03-18 | 2022-06-10 | 北京字节跳动网络技术有限公司 | Video pushing method, device, equipment and storage medium |
| CN114661949A (en) * | 2022-03-31 | 2022-06-24 | 南京硕茂电子科技有限公司 | Client data analysis system and method under big data situation |
| CN115714884A (en) * | 2022-11-18 | 2023-02-24 | 天津智融创新科技发展有限公司 | Video loading method and device |
| CN116777529A (en) * | 2023-08-11 | 2023-09-19 | 腾讯科技（深圳）有限公司 | Object recommendation method, device, equipment, storage medium and program product |
| CN117440182A (en) * | 2023-10-25 | 2024-01-23 | 北京华星酷娱文化传媒有限公司 | Intelligent recommendation method and system based on video content analysis and user labels |
| CN119397054A (en) * | 2024-10-11 | 2025-02-07 | 郑州航空工业管理学院 | A multi-level data optimization method and system based on big data |
-
2021
- 2021-10-26 CN CN202111250121.8A patent/CN114154013A/en active Pending
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114615525A (en) * | 2022-03-18 | 2022-06-10 | 北京字节跳动网络技术有限公司 | Video pushing method, device, equipment and storage medium |
| CN114661949A (en) * | 2022-03-31 | 2022-06-24 | 南京硕茂电子科技有限公司 | Client data analysis system and method under big data situation |
| CN114661949B (en) * | 2022-03-31 | 2023-11-24 | 布谷鸟数字科技(中山)股份有限公司 | Client data analysis system and method under big data situation |
| CN115714884A (en) * | 2022-11-18 | 2023-02-24 | 天津智融创新科技发展有限公司 | Video loading method and device |
| CN116777529A (en) * | 2023-08-11 | 2023-09-19 | 腾讯科技（深圳）有限公司 | Object recommendation method, device, equipment, storage medium and program product |
| CN116777529B (en) * | 2023-08-11 | 2024-02-06 | 腾讯科技（深圳）有限公司 | Object recommendation method, device, equipment, storage medium and program product |
| CN117440182A (en) * | 2023-10-25 | 2024-01-23 | 北京华星酷娱文化传媒有限公司 | Intelligent recommendation method and system based on video content analysis and user labels |
| CN117440182B (en) * | 2023-10-25 | 2024-06-07 | 北京华星酷娱文化传媒有限公司 | Intelligent recommendation method and system based on video content analysis and user labels |
| CN119397054A (en) * | 2024-10-11 | 2025-02-07 | 郑州航空工业管理学院 | A multi-level data optimization method and system based on big data |
| CN119397054B (en) * | 2024-10-11 | 2025-09-09 | 郑州航空工业管理学院 | Multi-level data optimization method and system based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109819284B (en) | Short video recommendation method and device, computer equipment and storage medium | |
| CN114154013A (en) | Video recommendation method, device, equipment and storage medium | |
| CN108776676B (en) | Information recommendation method and device, computer readable medium and electronic device | |
| US10592565B2 (en) | Method and apparatus for providing recommended information | |
| CN113312512B (en) | Training method, recommending device, electronic equipment and storage medium | |
| US8645409B1 (en) | Contextual search term evaluation | |
| CN112818224A (en) | Information recommendation method and device, electronic equipment and readable storage medium | |
| CN113079417A (en) | Method, device and equipment for generating bullet screen and storage medium | |
| CN112860929B (en) | Picture searching method and device, electronic equipment and storage medium | |
| CN111324804A (en) | Search keyword recommendation model generation method, keyword recommendation method and device | |
| KR102712013B1 (en) | Method and device for transmitting information | |
| CN107609192A (en) | The supplement searching method and device of a kind of search engine | |
| CN112765478A (en) | Method, apparatus, device, medium, and program product for recommending content | |
| CN111782850B (en) | Object searching method and device based on hand drawing | |
| WO2022001349A1 (en) | Method and device for information analysis | |
| CN114461919A (en) | Information recommendation model training method and device | |
| CN112579729A (en) | Training method and device for document quality evaluation model, electronic equipment and medium | |
| CN110008396B (en) | Object information pushing method, device, equipment and computer readable storage medium | |
| KR101873339B1 (en) | System and method for providing interest contents | |
| CN115017200A (en) | Search result sorting method and device, electronic equipment and storage medium | |
| CN113360761A (en) | Information flow recommendation method and device, electronic equipment and computer-readable storage medium | |
| CN113051481A (en) | Content recommendation method and device, electronic equipment and medium | |
| CN113365138A (en) | Content presentation method, content presentation device, electronic device, storage medium, and program product | |
| CN113127683A (en) | Content recommendation method and device, electronic equipment and medium | |
| CN111144122B (en) | Evaluation processing method, device, computer system and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |