CN113051481A - Content recommendation method and device, electronic equipment and medium - Google Patents
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Abstract
The disclosure discloses a content recommendation method, device, equipment, medium and product, and relates to the fields of big data, intelligent recommendation and the like. The content recommendation method comprises the following steps: acquiring initial comment data for a historical video of a target user; determining a target video segment from the historical video based on the target comment data in the initial comment data; obtaining content to be recommended for a target user based on the target video clip; and recommending the content to be recommended in response to receiving an access request, wherein the access request is a request initiated aiming at the associated content of the target user.
Description
Technical Field
The present disclosure relates to the field of computer technologies, particularly to the field of big data and intelligent recommendation, and more particularly, to a content recommendation method, a content recommendation apparatus, an electronic device, a medium, and a program product.
Background
In the related art, a target user may upload a work created by the target user to a network for a viewer to watch, wherein the created work includes pictures, videos, documents and the like. However, it is difficult for the related art to accurately obtain the features of each target user, which makes it difficult to publicize for each target user, and thus makes the viewing rate of the works of the target users low. In addition, when a viewer is viewing a work of a target user, related techniques may recommend related content for the viewer, such as advertisements for the viewer. However, the related art does not associate the recommended advertisement with the content of the work when the advertisement is recommended, thereby making it difficult for the recommended advertisement to meet the viewer's demand.
Disclosure of Invention
The disclosure provides a content recommendation method, apparatus, electronic device, storage medium, and program product.
According to an aspect of the present disclosure, there is provided a content recommendation method including: aiming at a historical video of a target user, acquiring initial comment data aiming at the historical video; determining a target video segment from the historical video based on target comment data in the initial comment data; obtaining content to be recommended for the target user based on the target video clip; recommending the content to be recommended in response to receiving an access request, wherein the access request is a request initiated aiming at the associated content of the target user.
According to another aspect of the present disclosure, there is provided a content recommendation apparatus including: the device comprises a first obtaining module, a first determining module, a second obtaining module and a recommending module. 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 initial comment data aiming at a historical video of a target user; a first determining module, configured to determine a target video segment from the historical video based on target comment data in the initial comment data; the second obtaining module is used for obtaining the content to be recommended for the target user based on the target video clip; and the recommending module is used for recommending the content to be recommended in response to receiving an access request, wherein the access request is a request initiated aiming at the associated content of the target user.
According to another aspect of the present disclosure, there is provided an electronic device including: 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 content recommendation method described above.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the content recommendation method described above.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the content recommendation method described above.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 schematically illustrates an application scenario of a content recommendation method and apparatus according to an embodiment of the present disclosure;
FIG. 2 schematically shows a flow diagram of a content recommendation method according to an embodiment of the present disclosure;
FIG. 3 schematically shows a flow chart of a content recommendation method according to another embodiment of the present disclosure;
FIG. 4 schematically shows a diagram of a content recommendation method according to an embodiment of the present disclosure;
FIG. 5 schematically shows a block diagram of a content recommendation device according to an embodiment of the present disclosure; and
FIG. 6 is a block diagram of an electronic device for performing content recommendation used to implement an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. 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 disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
An embodiment of the present disclosure provides a content recommendation method, including: and acquiring initial comment data aiming at the historical video of the target user. Then, based on the target comment data in the initial comment data, a target video segment is determined from the historical video. And then, based on the target video clip, obtaining the content to be recommended for the target user, and recommending the content to be recommended in response to receiving an access request, wherein the access request is a request initiated by the associated content for the target user.
Fig. 1 schematically illustrates an application scenario of a content recommendation method and apparatus according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of an application scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the application scenario 100 according to this embodiment may include a server 100A, a client 100B, and a network 100C. Network 100C serves as a medium for providing communication links between server 100A and client 100B. Network 100C may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use client 100B to interact with server 100A over network 100C to receive or send messages, etc. Client 100B may have installed thereon various messenger client applications such as, for example only, a shopping-like application, a web browser application, a search-like application, an instant messaging tool, a mailbox client, social platform software, and the like.
For example, the client 100B may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablets, laptop portable computers, desktop computers, and the like. The client 100B of the disclosed embodiment may, for example, play videos and display related content.
The server 100A may be a server that provides various services, such as a background management server (for example only) that provides support for websites browsed by users using the client 100B. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the client. In addition, the server 100A may also be a cloud server, that is, the server 100A has a cloud computing function.
Illustratively, the target user uploads at least one historical video, and when any one or more of the at least one historical video is played, the viewer can comment on the played video. Taking the history video 101 as an example, when the client 100B plays the history video 101, the viewer may comment on the history video 101. The client 100B may store the initial comment data. The initial comment data may include, for example, a first-type comment data 102 and a second-type comment data 103.
The first type of comment data 102 is, for example, data displayed in a comment area, and the first type of comment data 102 generally requires a viewer to actively go to the comment area for browsing. The second type comment data 103 is comment data transmitted by a bullet screen method, for example, the second type comment data 103 is actively displayed in a display area of a played history video, for example, and a viewer can passively receive the second type comment data 103. The association between the second comment data 103 and the played history video is strong.
After the server 100A obtains the content to be recommended 104 for the target user, when the subsequent server 100A detects that the viewer accesses the associated content of the target user, the content to be recommended 104 may be sent to the client 100B for presentation, so as to recommend the content to be recommended 104 for the viewer. The associated content of the target user includes, but is not limited to, the content uploaded by the target user, the user home page 106 of the target user. Uploaded content includes, for example, historical videos 105, pictures, documents, and so forth.
Taking the associated content as the history video 105 as an example, when a subsequent viewer accesses or views any history video 105 uploaded by the target user, the server 100A sends the content to be recommended 104 to the client 100B, so as to display the recommended content 104 on the display page of the history video 105.
Taking the associated content as the user homepage 106 as an example, when a subsequent viewer accesses the user homepage 106 of the target user, the server 100A sends the content to be recommended 104 to the client 100B to display the recommended content 104 on the display page of the user homepage 106.
It should be noted that the content recommendation method provided by the embodiment of the present disclosure may be executed by the server 100A. Accordingly, the content recommendation device provided by the embodiment of the present disclosure may be provided in the server 100A. The content recommendation method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster different from the server 100A and capable of communicating with the client 100B and/or the server 100A. Accordingly, the content recommendation device provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 100A and capable of communicating with the client 100B and/or the server 100A.
It should be understood that the number of clients, networks, and servers in FIG. 1 is merely illustrative. There may be any number of clients, networks, and servers, as desired for an implementation.
The embodiment of the present disclosure provides a content recommendation method, and a content recommendation method according to an exemplary embodiment of the present disclosure is described below with reference to fig. 2 to 4 in conjunction with an application scenario of fig. 1. The content recommendation method of the embodiment of the present disclosure may be executed by the server 100A shown in fig. 1, for example.
Fig. 2 schematically shows a flow chart of a content recommendation method according to an embodiment of the present disclosure.
As shown in fig. 2, the content recommendation method 200 of the embodiment of the present disclosure may include, for example, operations S210 to S240.
In operation S210, initial comment data for a history video of a target user is acquired.
In operation S220, a target video segment is determined from the history video based on the target comment data in the initial comment data.
In operation S230, content to be recommended for a target user is obtained based on the target video clip.
In operation S240, in response to receiving the access request, the content to be recommended is recommended.
The historical video for the target user is, for example, a video uploaded by the target user, and the historical video may include one or more videos. For example, for all historical videos of a target user, initial comment data for all historical videos may be obtained, target comment data may be determined from the initial comment data, and then at least one video clip may be determined from all historical videos as a target video clip based on the target comment data.
After the target video segment is determined, the content to be recommended for the target user can be obtained based on the target video segment. In an example, the target video segment may be processed to obtain the content to be recommended. In another example, the target video clip and the content stored in the database may be matched to determine the content associated with the target video clip from the database as the content to be recommended.
After the content to be recommended is obtained, whether an access request is received or not can be detected in real time, wherein the access request is a request initiated aiming at the associated content of the target user. If an access request is received, the content to be recommended can be recommended.
According to the embodiment of the disclosure, the target video segment is determined based on the initial comment data for the historical video, and then the content to be recommended for the target user is obtained based on the target video segment. Since the content to be recommended is obtained based on the historical video of the target user, the content to be recommended is associated with the target user. By recommending the content to be recommended, the targeted propaganda for each target user is realized, so that the viewer can quickly know the user characteristics of the target user, and the interest degree of the viewer in the associated content of the target user is improved. In addition, the recommended content to be recommended is associated with the content of the history video, so that the recommended content to be recommended meets the needs of the viewer watching the history video.
Fig. 3 schematically shows a flow chart of a content recommendation method according to another embodiment of the present disclosure.
As shown in fig. 3, the content recommendation method 300 of the embodiment of the present disclosure may include, for example, operations S301 to S312.
In operation S301, initial comment data for a history video of a target user is acquired.
For example, the initial comment data includes a plurality of initial comment data. After the initial comment data is acquired, target comment data may be determined from a plurality of initial comment data. Determining the target comment data from the plurality of initial comment data includes, for example, operations S302 to S304.
In operation S302, a plurality of initial comment data are classified, resulting in a plurality of categories.
In operation S303, a target category is determined from a plurality of categories based on the number of initial comment data in each category.
In operation S304, the initial comment data in the target category is taken as target comment data.
And classifying the plurality of initial comment data according to the content of each initial comment data to obtain a plurality of categories. The plurality of initial review data may be classified using a classification model, for example.
Each category includes, for example, at least one piece of initial comment data, and the pieces of initial comment data belonging to the same category are similar to each other, for example, the pieces of initial comment data belonging to the same category have the same keyword therebetween. Next, one or more categories including the largest amount of initial comment data among the plurality of categories are set as target categories. Then, the initial comment data included in the target category is taken as target comment data.
The target comment data is data that the viewer has commented on the historical video, and therefore indicates the point of interest of the viewer on the historical video, and generally indicates the user characteristics of the target user who uploaded the historical video. For example, history videos uploaded by target users include gourmet videos, which are often accompanied by exaggerated expressions and a fun of the target users when the target users start eating, and many viewers may initiate a lot of comments to be taken while the history videos are played to the exaggerated expressions and the fun of the fun, and the initiated comments are similar in content, for example. It follows that target comment data, such as data for exaggerated expressions and a feeling of fun, which indicates the user characteristics of the target user, that is, the user characteristics of the target user including the exaggerated expressions and the feeling of fun, for example, can be determined from all the initial comment data for the history video.
Next, after determining the target comment data, determining the target video segment from the history video based on the target comment data includes, for example, operations S305 to S307.
In operation S305, key information is extracted from the target comment data.
In operation S306, at least one candidate video clip including key information is determined from the historical video.
In operation S307, a target video clip is selected from at least one candidate video clip.
The key information extracted from the target comment data includes, for example, the subject content to which the target comment data relates or the keywords included in the target comment data.
In an embodiment, candidate video clips associated with the key information are extracted based on image information of the historical video. For example, taking historical videos as food videos for example, the key information includes information related to "eating", for example. Based on the image information related to 'eating' in the historical video, video clips related to 'eating' are extracted from the historical video to serve as candidate video clips, and then better candidate video clips are selected from the candidate video clips to serve as target video clips.
In another embodiment, candidate video snippets associated with the key information are extracted based on the audio information of the historical video. For example, taking a historical video as a food video as an example, the key information includes information related to "eating", for example, based on audio information related to "eating" in the historical video, a video clip corresponding to the audio information is extracted from the historical video as a candidate video clip, and then a better candidate video clip is selected from the candidate video clips as a target video clip.
Next, after obtaining the target video segment, obtaining the content to be recommended for the target user based on the target video segment, for example, includes operations S308 to S310.
In operation S308, the target video segment is processed to obtain the description content for the target user.
For example, the target video segment includes a plurality of frames of images, and at least one frame of image for the target user is selected from the plurality of frames of images. Then, at least one frame of image is processed to obtain the description content aiming at the target user. The descriptive content includes, for example, an image for the target user, a motion picture for the target user, or a video for the target user.
The at least one frame image includes, for example, a plurality of frame images. After extracting the multi-frame images related to the target user in the target video clip, performing cutting, rendering, synthesizing, deformation and other processing on the multi-frame images so as to perform exaggeration processing on the image of the target user, and also matching characters, thereby obtaining description contents representing the user characteristics of the target user. The descriptive content can be used for targeted publicity for the target user, so that the viewer can quickly know the user characteristics of the target user, and the viewing rate of the content uploaded by the target user is improved.
In operation S309, a target advertisement associated with the descriptive content is determined from the at least one candidate advertisement based on the descriptive content.
For example, when the descriptive content is related to a food, a targeted advertisement related to the food may be selected from a plurality of candidate advertisements stored in the database, the targeted advertisement being, for example, "XX olive oil".
In operation S310, at least one of the content and the target advertisement will be described as the content to be recommended.
In an example, the description content may be taken as the content to be recommended, or the target advertisement may be taken as the content to be recommended, and the description content and the target advertisement may also be taken as the content to be recommended together. For example, when the description content and the target advertisement are taken together as the content to be recommended, the target advertisement may be attached to the description content, for example, an identifier of "XX olive oil" may be attached to the description content.
In operation S311, it is determined whether an access request is received. If so, operation S312 is performed. If not, return to perform operation S311.
In operation S312, in response to receiving the access request, the content to be recommended is recommended.
For example, the associated content of the target user includes a user homepage of the target user or content uploaded by the target user. The content uploaded by the target user includes pictures, videos, documents, and the like. When the viewer accesses the user homepage of the target user or the uploaded content, the content to be recommended may be recommended to the viewer.
According to the embodiment of the disclosure, the description information for the target user is acquired as the content to be recommended, and the content to be recommended is recommended to the viewer. The content to be recommended represents the user characteristics of the target user, and can be used for carrying out targeted propaganda on the target user, so that a viewer can quickly know the user characteristics of the target user, and the viewing rate of the content uploaded by the target user is improved.
In addition, targeted advertisements may also be determined based on the descriptive content, the determined targeted advertisements being associated with the descriptive content such that the targeted advertisements are associated with the targeted users. Then, the description content and the target advertisement are taken as the content to be recommended, and the content to be recommended is recommended to the viewer. It can be understood that the propaganda of the target users and the target advertisements is realized simultaneously through the embodiment of the disclosure, so that the recommended content to be recommended better meets the requirements of viewers, and the propaganda effect is improved.
Fig. 4 schematically shows a schematic diagram of a content recommendation method according to an embodiment of the present disclosure.
As shown in fig. 4, the plurality of initial comment data include, for example, a plurality of initial comment data 402A for the history video 401A, a plurality of initial comment data 402B for the history video 401B, and a plurality of initial comment data 402C for the history video 401C for the plurality of history videos 401A, 401B, and 401C of the target user.
The multiple initial comment data are divided to obtain multiple categories 403A, 403B, and 403C, and each category includes at least one initial comment data. One or more categories having the most initial comment data are set as target categories, for example, the category 403B is set as a target category. Then, the initial comment data in the category 403B (target category) is taken as target comment data.
Then, key information 404 is extracted from the target comment data, and at least one candidate video clip including the key information 404 is determined from the plurality of history videos 401A, 401B, 401C. The at least one candidate video segment includes, for example, a plurality of candidate video segments 405A, 405B, 405C.
Next, a target video segment is determined from the plurality of candidate video segments 405A, 405B, 405C, e.g., candidate video segment 405B is determined as the target video segment. Determining the target video segment from the at least one candidate video segment includes, for example, at least the following three ways.
First, the at least one candidate video segment includes, for example, a plurality of candidate video segments. And performing quality evaluation on each candidate video clip in the candidate video clips by using a quality evaluation model to obtain a plurality of evaluation values corresponding to the candidate video clips one by one. In an example, a higher rating value indicates that the integrity of the candidate video segment is higher, and the high integrity indicates that the video segment is a segment for one scene, for example, a video segment for a "eat" scene. In another example, the higher the rating value, the lower the volatility representing the candidate video segment, and the low volatility characterizes the video segment as being directed to only one scene, e.g., to a "dining" scene, rather than a mixture of multiple scenes.
After the plurality of evaluation values are obtained, a target evaluation value is selected from the plurality of evaluation values, and for example, the largest evaluation value is selected as the target evaluation value from the plurality of evaluation values. Then, a candidate video segment corresponding to the target evaluation value is selected from the plurality of candidate video segments as a target video segment.
In a second way, a selection operation from the target user is received, wherein the selection operation is an operation performed by the target user for at least one candidate video segment. Then, based on the selection operation, a target video segment is determined from the at least one candidate video segment.
In a third aspect, when the target evaluation value includes a plurality of target evaluation values, a plurality of candidate video segments corresponding one-to-one to the plurality of target evaluation values are determined. Then, based on a selection operation performed by a target user for a plurality of candidate video segments that correspond one-to-one to the plurality of target evaluation values, a target video segment is determined from the plurality of candidate video segments that correspond one-to-one to the plurality of target evaluation values based on the selection operation.
After the target video segment is determined, the target video segment is processed to obtain descriptive content 406 for the target user. Then, a targeted advertisement 408 associated with the descriptive content 406 is determined from the plurality of candidate advertisements 407 based on the descriptive content 406. The description content 406 and the targeted advertisement 408 are referred to as a content to be recommended 409 for recommendation.
For example, taking the historical video as the gourmet video as an example, when a target user in the gourmet video starts to eat, the target user may say, for example, a sentence "it is a little good today", at which time the target user starts to show an exaggerated expression and a fun, and many viewers may initiate a lot of comments to canon. The interested part of the viewer can be known based on the comment of the viewer, the target video segment aiming at the exaggerated expression and the fun of the target user can be extracted from the historical video, the target video segment is processed to obtain the description content, the description content can be used as the symbolic information of the target user, the target user can be publicized in a targeted mode through the description content, and the viewer can quickly know the characteristic of the target user based on the description content. In addition, a targeted advertisement may also be determined based on the descriptive content, the determined targeted advertisement being associated with the descriptive content such that the targeted advertisement is associated with the targeted user, the targeted advertisement being, for example, "XX olive oil". Then, the description content and the target advertisement are taken as the content to be recommended to the viewer, for example, the target advertisement may be attached to the description content, for example, an identifier of "XX olive oil" may be attached to the description content. Through the embodiment of the disclosure, propaganda of the target user and the target advertisement is realized at the same time, so that the recommended content to be recommended better meets the requirements of viewers.
Fig. 5 schematically shows a block diagram of a content recommendation device according to an embodiment of the present disclosure.
As shown in fig. 5, the content recommendation apparatus 500 of the embodiment of the present disclosure includes, for example, a first obtaining module 510, a first determining module 520, a second obtaining module 530, and a recommending module 540.
The first obtaining module 510 may be configured to obtain initial comment data for a historical video of a target user. According to an embodiment of the present disclosure, the first obtaining module 510 may perform, for example, the operation S210 described above with reference to fig. 2, which is not described herein again.
The first determination module 520 may be configured to determine a target video segment from the historical video based on the target commentary data in the initial commentary data. According to the embodiment of the present disclosure, the first determining module 520 may perform, for example, operation S220 described above with reference to fig. 2, which is not described herein again.
The second obtaining module 530 may be configured to obtain the content to be recommended for the target user based on the target video segment. According to the embodiment of the present disclosure, the second obtaining module 530 may, for example, perform operation S230 described above with reference to fig. 2, which is not described herein again.
The recommending module 540 may be configured to recommend the content to be recommended in response to receiving an access request, the access request being a request initiated for the associated content of the target user. According to an embodiment of the present disclosure, the recommending module 540 may, for example, perform operation S240 described above with reference to fig. 2, which is not described herein again.
According to an embodiment of the present disclosure, the second obtaining module 530 includes a processing sub-module, a first determining sub-module, and a second determining sub-module. And the processing submodule is used for processing the target video clip to obtain the description content aiming at the target user. A first determining sub-module for determining a targeted advertisement associated with the descriptive content from the at least one candidate advertisement based on the descriptive content. And the second determining submodule is used for taking at least one of the description content and the target advertisement as the content to be recommended.
According to an embodiment of the present disclosure, the target video segment includes a plurality of frames of images. The processing submodule comprises a first selection unit and a processing unit. The first selection unit is used for selecting at least one frame of image aiming at the target user from the multi-frame images. And the processing unit is used for processing at least one frame of image to obtain the description content.
According to an embodiment of the present disclosure, the first determination module 520 includes an extraction sub-module, a third determination sub-module, and a selection sub-module. And the extraction submodule is used for extracting key information from the target comment data. A third determining sub-module for determining at least one candidate video segment comprising key information from the historical video. A selection sub-module for selecting a target video segment from the at least one candidate video segment.
According to an embodiment of the present disclosure, the at least one candidate video segment includes a plurality of candidate video segments. The selection submodule comprises an evaluation unit, a second selection unit and a third selection unit. And the evaluation unit is used for evaluating the quality of each candidate video clip in the candidate video clips by using the quality evaluation model to obtain a plurality of evaluation values which are in one-to-one correspondence with the candidate video clips. A second selection unit configured to select a target evaluation value from the plurality of evaluation values. A third selecting unit configured to select, as the target video segment, a candidate video segment corresponding to the target evaluation value from among the plurality of candidate video segments.
According to an embodiment of the present disclosure, a selection submodule includes a receiving unit and a determining unit. A receiving unit, configured to receive a selection operation from a target user, where the selection operation is an operation performed by the target user for at least one candidate video segment. A determining unit for determining a target video segment from the at least one candidate video segment based on the selecting operation.
According to an embodiment of the present disclosure, the initial comment data includes a plurality of initial comment data. The apparatus 500 further includes a second determining module for determining target review data from the plurality of initial review data. The second determination module includes a classification sub-module, a fourth determination sub-module, and a fifth determination sub-module. And the classification submodule is used for classifying the initial comment data to obtain a plurality of classes. A fourth determination sub-module for determining a target category from the plurality of categories based on the number of initial review data in each category. And the fifth determining submodule is used for taking the initial comment data in the target category as target comment data.
According to an embodiment of the present disclosure, the description includes at least one of: an image for a target user; an animation for the target user; and, a video for the target user.
According to an embodiment of the present disclosure, the associated content of the target user includes at least one of: a user home page of the target user; and, the content uploaded by the target user.
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. 6 is a block diagram of an electronic device for performing content recommendation used to implement an embodiment of the present disclosure.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. The electronic device 600 is 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.
As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 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 calculation unit 601 performs the respective methods and processes described above, such as the content recommendation method. For example, in some embodiments, the content recommendation method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the content recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the content recommendation method in any other suitable way (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 or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
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 (21)
1. A content recommendation method, comprising:
aiming at a historical video of a target user, acquiring initial comment data aiming at the historical video;
determining a target video segment from the historical video based on target comment data in the initial comment data;
obtaining content to be recommended for the target user based on the target video clip; and
recommending the content to be recommended in response to receiving an access request, wherein the access request is a request initiated aiming at the associated content of the target user.
2. The method of claim 1, wherein the obtaining of the content to be recommended for the target user based on the target video segment comprises:
processing the target video clip to obtain the description content aiming at the target user;
determining a target advertisement associated with the descriptive content from at least one candidate advertisement based on the descriptive content; and
and taking at least one of the description content and the target advertisement as the content to be recommended.
3. The method of claim 2, wherein the target video segment comprises a plurality of frames of images; the processing the target video clip to obtain the description content for the target user comprises:
selecting at least one frame image for the target user from the plurality of frame images; and
and processing the at least one frame of image to obtain the description content.
4. The method of claim 1, wherein the determining a target video segment from the historical video based on target commentary data in the initial commentary data comprises:
extracting key information from the target comment data;
determining at least one candidate video segment comprising the key information from the historical video; and
selecting a target video segment from the at least one candidate video segment.
5. The method of claim 4, wherein the at least one candidate video segment comprises a plurality of candidate video segments;
wherein the selecting a target video segment from the at least one candidate video segment comprises:
performing quality evaluation on each candidate video clip in a plurality of candidate video clips by using a quality evaluation model to obtain a plurality of evaluation values corresponding to the candidate video clips one by one;
selecting a target evaluation value from the plurality of evaluation values; and
selecting a candidate video clip corresponding to the target evaluation value from the plurality of candidate video clips as the target video clip.
6. The method of claim 4, wherein said selecting a target video segment from the at least one candidate video segment comprises:
receiving a selection operation from the target user, wherein the selection operation is an operation performed by the target user for the at least one candidate video segment; and
determining a target video segment from the at least one candidate video segment based on the selecting operation.
7. The method of claim 1, the initial comment data comprising a plurality of initial comment data; the method further comprises the following steps: determining the target comment data from the plurality of initial comment data;
wherein the determining the target comment data from the plurality of initial comment data comprises:
classifying the plurality of initial comment data to obtain a plurality of categories;
determining a target category from a plurality of categories based on the amount of initial review data in each category; and
and taking the initial comment data in the target category as the target comment data.
8. The method of claim 2 or 3, wherein the descriptive content comprises at least one of:
an image for the target user;
an animation for the target user; and
a video for the target user.
9. The method of claim 1, wherein the associated content of the target user comprises at least one of:
a user homepage of the target user; and
the content uploaded by the target user.
10. A content recommendation apparatus comprising:
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 initial comment data aiming at a historical video of a target user;
a first determining module, configured to determine a target video segment from the historical video based on target comment data in the initial comment data;
the second obtaining module is used for obtaining the content to be recommended for the target user based on the target video clip; and
and the recommending module is used for recommending the content to be recommended in response to receiving an access request, wherein the access request is a request initiated aiming at the associated content of the target user.
11. The apparatus of claim 10, wherein the second obtaining means comprises:
the processing submodule is used for processing the target video clip to obtain the description content aiming at the target user;
a first determining sub-module, configured to determine, based on the descriptive content, a targeted advertisement associated with the descriptive content from at least one candidate advertisement; and
and the second determining submodule is used for taking at least one of the description content and the target advertisement as the content to be recommended.
12. The apparatus of claim 11, wherein the target video segment comprises a plurality of frames of images; the processing submodule comprises:
a first selection unit configured to select at least one frame image for the target user from the plurality of frame images; and
and the processing unit is used for processing the at least one frame of image to obtain the description content.
13. The apparatus of claim 10, wherein the first determining means comprises:
the extraction submodule is used for extracting key information from the target comment data;
a third determining sub-module for determining at least one candidate video segment including the key information from the historical video; and
a selection sub-module for selecting a target video segment from the at least one candidate video segment.
14. The apparatus of claim 13, wherein the at least one candidate video segment comprises a plurality of candidate video segments;
wherein the selection submodule comprises:
the evaluation unit is used for evaluating the quality of each candidate video clip in a plurality of candidate video clips by using a quality evaluation model to obtain a plurality of evaluation values which are in one-to-one correspondence with the candidate video clips;
a second selection unit configured to select a target evaluation value from the plurality of evaluation values; and
a third selecting unit configured to select, as the target video segment, a candidate video segment corresponding to the target evaluation value from the plurality of candidate video segments.
15. The apparatus of claim 13, wherein the selection submodule comprises:
a receiving unit, configured to receive a selection operation from the target user, where the selection operation is an operation performed by the target user for the at least one candidate video segment; and
a determining unit, configured to determine a target video segment from the at least one candidate video segment based on the selecting operation.
16. The apparatus of claim 10, the initial comment data comprising a plurality of initial comment data; the device further comprises: a second determining module for determining the target comment data from the plurality of initial comment data;
wherein the second determining module comprises:
the classification submodule is used for classifying the plurality of initial comment data to obtain a plurality of categories;
a fourth determination sub-module for determining a target category from the plurality of categories based on the number of initial comment data in each category; and
and the fifth determining submodule is used for taking the initial comment data in the target category as the target comment data.
17. The apparatus of claim 11 or 12, wherein the descriptive content comprises at least one of:
an image for the target user;
an animation for the target user; and
a video for the target user.
18. The apparatus of claim 10, wherein the associated content of the target user comprises at least one of:
a user homepage of the target user; and
the content uploaded by the target user.
19. 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 1-9.
20. 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 1-9.
21. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-9.
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