CN113127683A - 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: obtaining comment data aiming at a target video, wherein the comment data are used for representing the attention of the target video; determining a target video clip from the target video based on the comment data, wherein the attention of the target video clip meets a preset attention condition; determining content to be recommended associated with the comment data from the at least one candidate content; and recommending the content to be recommended in response to detecting that the target video is played to the target video segment.
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, when a user watches a video, related content, such as advertisements, may be recommended to the user. However, in the related art, when content is recommended, the recommended content is not associated with the content of the video, so that the recommended content is more obtrusive. In addition, the related art does not recommend based on the user's attention when recommending content, so that the recommended content hardly satisfies the user's needs.
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: obtaining comment data aiming at a target video, wherein the comment data are used for representing the attention of the target video; determining a target video segment from the target video based on the comment data, wherein the attention of the target video segment meets a preset attention condition; determining content to be recommended associated with the comment data from at least one candidate content; recommending the content to be recommended in response to detecting that the target video is played to the target video clip.
According to another aspect of the present disclosure, there is provided a content recommendation apparatus including: the device comprises an acquisition module, a first determination module, a second determination module and a recommendation module. The device comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring comment data aiming at a target video, and the comment data is used for representing the attention of the target video; a first determining module, configured to determine a target video segment from the target video based on the comment data, where a degree of attention of the target video segment satisfies a preset degree of attention condition; a second determination module, configured to determine, from at least one candidate content, a content to be recommended that is associated with the comment data; and the recommending module is used for recommending the content to be recommended in response to the fact that the target video is detected to be played to the target video segment.
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 obtaining comment data aiming at the target video, wherein the comment data are used for representing the attention of the target video. Then, based on the comment data, a target video segment is determined from the target video, and the attention of the target video segment meets a preset attention condition. Next, a content to be recommended associated with the comment data is determined from the at least one candidate content, and in response to detecting that the target video is played to the target video segment, the content to be recommended is recommended.
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 play a video, for example.
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, a user may comment on a target video 101 played by a client 100B while watching the target video 101. The client 100B may store comment data of the user. The 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 user to actively go to the comment area for browsing. The second type of comment data 103 is, for example, comment data transmitted by a bullet screen method, the second type of comment data 103 is, for example, actively displayed in a display area of the target video 101, and a user viewing the target video 101 can passively receive the second type of comment data 103. The association between the second comment data 103 and the target video 101 is strong.
The server 100A may obtain comment data of the user from at least one client, including the client 100B. Then, the server 100A processes the comment data to determine the content to be recommended 104 associated with the comment data. The comment data acquired by the server 100A is, for example, history data, that is, data transmitted when a plurality of users viewed the target video 101 before.
For example, after the server 100A acquires the comment data, the content to be recommended 104 associated with the comment data may be selected from a database, and the content to be recommended 104 may be, for example, advertisement content. When the subsequent server 100A detects that the client 100B plays the target video 101 again, the server 100A may recommend the content 104 to be recommended to the client 100B, so that the client 100B presents the content 104 to be recommended, and the content 104 to be recommended is recommended to the user.
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, comment data for a target video is acquired.
In operation S220, a target video segment is determined from the target video based on the comment data.
In operation S230, a content to be recommended associated with the comment data is determined from the at least one candidate content.
In operation S240, in response to detecting that the target video is played to the target video clip, content to be recommended is recommended.
For example, the commentary data for the target video may be used to characterize the attention of the target video. When the number of comment data for a target video is larger, the degree of attention representing the target video is higher. After the comment data for the target video is acquired, a target video segment whose attention degree satisfies a preset attention degree condition may be determined from the target video based on the comment data, and the determined attention degree of the target video segment is, for example, high.
Next, a content to be recommended associated with the comment data may be determined from a plurality of candidate contents stored in the database. Illustratively, the subject of the content to be recommended is, for example, consistent with the subject of the comment data, or the content to be recommended has related information of the comment data.
Due to the fact that the attention degree of the target video clip is high, when the target video is played again subsequently, when the fact that the target video clip is played is detected, the content to be recommended can be recommended, and therefore the content to be recommended has the high attention degree.
According to the embodiment of the disclosure, the comment data of the target video is acquired, and the target video segment with high attention is determined based on the comment data. And when the target video is played again subsequently, recommending the content to be recommended associated with the comment data when the target video segment is detected to be played. It can be understood that the comment data embodies the content of the target video to a certain extent, so that the content to be recommended associated with the comment data is recommended, and the association degree between the content to be recommended and the content of the target video is improved. In addition, because the attention degree of the target video clip is high, the content to be recommended is recommended when the target video clip is played, so that the content to be recommended has higher attention degree.
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 S309.
In operation S301, comment data for a target video is acquired.
In operation S302, a plurality of video clips are determined from a target video.
In operation S303, a target video segment is determined from among a plurality of video segments for a target video based on the comment data.
In an embodiment, the target video may be divided into a plurality of video segments that do not overlap. For example, when the duration of the target video is 10 minutes, the target video is divided into 20 video segments, each video segment has a duration of 30 seconds, and the contents of each video segment do not overlap with each other.
In another example, multiple video clips may be extracted from the target video, and overlapping content may or may not exist for any two of the multiple video clips. For example, when the duration of the target video is 10 minutes, a video clip of the first 30 seconds is extracted as a first video clip, a video clip of the 6 th to 35 th seconds is extracted as a second video clip, a video clip of the 11 th to 40 th seconds is extracted as a third video clip, and so on to obtain a plurality of video clips. The overlapping content of any two of the plurality of video segments may exist, for example, the overlapping content of the second video segment and the third video segment is the 11 th to 35 th second video content.
After determining a plurality of video segments from the target video, a video segment with a focus satisfying a preset focus condition may be determined from the plurality of video segments as the target video segment.
In an embodiment, the target video segment whose attention degree satisfies the preset attention degree condition includes, for example, a number of comment data for the target video segment that is greater than a preset number. That is, the obtained comment data is for the target video, and each video clip corresponds to a corresponding amount of comment data. One or more video segments with the number of the comment data larger than the preset number can be determined from the plurality of video segments as the target video segments based on the number of the comment data corresponding to each video segment.
In another embodiment, the number of comment data for the target video segment is greater than the number of comment data for the remaining video segments, the remaining video segments being ones of the plurality of video segments other than the target video segment. That is, one or more video clips having the largest amount of comment data among the plurality of video clips are determined as the target video clip.
In operation S304, candidate comment data for the target video segment is determined from the plurality of comment data.
In operation S305, a plurality of candidate comment data are classified, resulting in a plurality of categories.
In operation S306, a target category is determined from a plurality of categories based on the number of candidate comment data in each category.
In operation S307, a content to be recommended is determined from the at least one candidate content based on the target category.
The comment data for the target video includes, for example, a plurality of comment data. Each comment data includes, for example, a comment time, and from the comment time of each comment data, it can be determined for which video segment each comment data is. The comment time includes, for example, a comment time, and when the comment time of a certain comment data is within a time length range of a certain video segment, it indicates that the comment data is data for the video segment.
A plurality of candidate comment data for the target video clip are extracted from the plurality of comment data on the basis of the comment time of each comment data and the duration information of each video clip. Then, according to the content of each candidate comment data, classifying the candidate comment data into a plurality of categories. The plurality of candidate opinion data may be classified, for example, using a classification model.
Each category includes, for example, at least one candidate comment data, and candidate comment data belonging to the same category are similar to each other. For example, a plurality of candidate comment data belonging to a certain category are data related to food.
Next, the category with the largest number of candidate comment data among the plurality of categories is set as the target category. And determining the content to be recommended associated with the candidate comment data from the plurality of candidate contents based on a fuzzy matching algorithm aiming at the candidate comment data included in the target category.
For example, the plurality of candidate content includes a first candidate content related to food, a second candidate content related to sports, and a third candidate content related to travel. When the candidate comment data in the target category are all food-related data, determining first candidate content associated with the target category as content to be recommended based on a fuzzy matching algorithm.
In another example, key information, such as keywords, may also be extracted from the candidate comment data for the target category. For example, when the candidate comment data in the target category are all food-related data, the extracted key information is, for example, "XX beverage". Next, candidate content containing key information is selected from at least one candidate content as content to be recommended. For example, a candidate content including "XX drink" is selected as the content to be recommended from among the plurality of candidate contents.
In the embodiment of the disclosure, after a plurality of candidate comment data for a target video segment are acquired, the candidate comment data are further subjected to category division to determine a target category with the largest number of candidate comment data. Because the attention degree of the target video clip is high, and the candidate comment data in the target category represent the main attention point for the target video clip, the associated content to be recommended is determined based on the candidate comment data in the target category, and the attention degree of the content to be recommended can be improved.
In operation S308, it is detected whether the target video is played to the target video segment. If so, operation S309 is performed. If not, return to perform operation S308.
In operation S309, in response to detecting that the target video is played to the target video clip, content to be recommended is recommended.
And after the target video clip and the content to be recommended are determined, detecting whether the target video is played to the target video clip in real time when the target video is played again subsequently. And if the target video is played to the target video segment, recommending the content to be recommended.
According to the embodiment of the disclosure, the comment data of the target video is acquired, and the target video segment with high attention is determined based on the comment data. Then, the content to be recommended associated with the target video segment is determined based on the candidate comment data for the target video segment. And when the target video is played again subsequently, recommending the content to be recommended associated with the comment data when the target video segment is detected to be played. It can be understood that the comment data reflects the content of the target video to a certain extent, and for a target video segment with high attention, the content to be recommended is determined based on the candidate comment data for the target video segment, so that the association degree between the content to be recommended and the target video segment is improved, and the attention degree of the content to be recommended is higher. In addition, because the attention degree of the target video clip is high, the content to be recommended is recommended when the target video clip is played, so that the attention degree of the content to be recommended is further improved, and the content to be recommended better meets the requirements of users.
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 target video 401 has a plurality of video clips, for example, and the video clips 401A, 401B, and 401C are described as an example. The comment data for the target video includes, for example, comment data for each video clip. For example, the comment data for the target video includes a plurality of comment data 402A for the video segment 401A, a plurality of comment data 402B for the video segment 401B, and a plurality of comment data 402C for the video segment 401C.
Based on the number of comment data, a target video segment is determined from the plurality of video segments 401A, 401B, 401C. For example, the number of the plurality of comment data 402B is larger than the number of the plurality of comment data 402A, and the number of the plurality of comment data 402B is larger than the number of the plurality of comment data 402C, and the video clip 401B corresponding to the plurality of comment data 402B may be determined as the target video clip.
Next, a plurality of comment data 402B for a video clip 401B (target video clip) is divided as candidate comment data, resulting in a plurality of categories 403A, 403B, 403C, each category including at least one candidate comment data. One or more categories with the most candidate comment data are set as target categories, for example, the category 403B is set as a target category. Then, based on candidate comment data in the category 403B (target category), a content to be recommended 405 is determined from the plurality of candidate contents 404. The content to be recommended 405 is associated with candidate comment data in the category 403B (target category).
Illustratively, the candidate content is, for example, advertising content including, for example, videos, pictures, icons, texts, and the like. And selecting the advertisement content associated with the candidate comment data in the target category from the plurality of advertisement contents to recommend the advertisement content.
For example, taking the target video as the gourmet video as an example, when a resident starts to eat in the gourmet video, the resident openly starts to take off the glasses, and the action of taking off the glasses to prepare to eat is taken as a feature of the resident. When the video is played to a video clip (target video clip) for which the glasses are picked to eat, a lot of watching users can initiate a lot of comments to canon. By obtaining the comment data, the video clip for eating prepared for picking glasses can be known, and the comment amount of the user is large. At this time, comment data of this video clip prepared for eating with the glasses in the abstract may be used as candidate comment data. Then, the candidate comment data is classified into a plurality of categories, and the category for "glasses" is set as the target category when it is found that there is a large amount of comment data for "glasses" in the plurality of categories. Then, based on the target category, the content to be recommended associated with the "glasses" is determined from the plurality of candidate contents for recommendation. The content to be recommended includes, for example, advertisement content including, for example, videos, pictures, icons, texts, and the like associated with "glasses". For example, "glasses + removal" is extracted from candidate comment data in the target category as key information, the key information is matched with candidate content stored in the database to obtain content to be recommended, and the content to be recommended obtained through matching is, for example, "XXX laser treatment is used for myopia", "removal of glasses is not a dream", and the like. When the food video is played again later, when the video is played to a video clip for taking glasses to prepare for eating, the 'XXX laser treatment myopia' and 'not dreams' can be recommended when the glasses are taken off.
It can be appreciated that compared to recommending pre-posted, pause, post-posted ads while the video is playing, the content recommendation scheme of the embodiments of the present disclosure takes into account the content of the video and the comment data of the user sufficiently, so that the recommended content (ads) is associated with the video content and meets the needs of the user.
Illustratively, a pre-posted ad is, for example, an ad that is recommended before the video is played, a pause ad is, for example, an ad that is recommended when the video is paused, and a post-posted ad is, for example, an ad that is recommended after the video is played.
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, an acquisition module 510, a first determination module 520, a second determination module 530, and a recommendation module 540.
The obtaining module 510 may be configured to obtain comment data for the target video, where the comment data is used to characterize the attention of the target video. According to the embodiment of the present disclosure, the 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 determining module 520 may be configured to determine a target video segment from the target video based on the comment data, where a degree of attention of the target video segment satisfies a preset degree of attention condition. 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 determining module 530 may be configured to determine the content to be recommended associated with the comment data from the at least one candidate content. According to an embodiment of the present disclosure, the second determining module 530 may perform, for example, the 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 detecting that the target video is played to the target video clip. 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 comment data includes a plurality of comment data; the second determination module 530 includes a first determination submodule and a second determination submodule. A first determining sub-module for determining candidate comment data from the plurality of comment data, wherein the candidate comment data is data for the target video segment. And the second determining sub-module is used for determining the content to be recommended associated with the candidate comment data from at least one candidate content.
According to an embodiment of the present disclosure, the candidate comment data includes a plurality of candidate comment data; the second determination submodule includes a classification unit, a first determination unit, and a second determination unit. And the classification unit is used for classifying the candidate comment data to obtain a plurality of classes. A first determination unit configured to determine a target category from a plurality of categories based on the number of candidate comment data in each category. And the second determining unit is used for determining the content to be recommended from at least one candidate content based on the target category, wherein the content to be recommended is associated with the candidate comment data in the target category.
According to an embodiment of the present disclosure, the second determination unit includes: an extraction subunit and a selection subunit. And the extracting subunit is used for extracting the key information from the candidate comment data of the target category. And the selecting subunit is used for selecting the candidate content containing the key information from at least one candidate content as the content to be recommended.
According to an embodiment of the present disclosure, the condition that the attention of the target video segment satisfies the preset attention condition includes at least one of the following: the number of the comment data for the target video clip is greater than a preset number; and the number of the comment data for the target video segment is larger than the number of the comment data for the remaining video segments, wherein the remaining video segments are the video segments of the target video other than the target video segment.
According to an embodiment of the present disclosure, the content to be recommended includes advertisement content.
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 (15)
1. A content recommendation method, comprising:
obtaining comment data aiming at a target video, wherein the comment data are used for representing the attention of the target video;
determining a target video segment from the target video based on the comment data, wherein the attention of the target video segment meets a preset attention condition;
determining content to be recommended associated with the comment data from at least one candidate content; and
recommending the content to be recommended in response to detecting that the target video is played to the target video clip.
2. The method of claim 1, wherein the comment data includes a plurality of comment data; the determining of the content to be recommended associated with the comment data from the at least one candidate content includes:
determining candidate commentary data from the plurality of commentary data, wherein the candidate commentary data is data for the target video clip; and
and determining the content to be recommended associated with the candidate comment data from at least one candidate content.
3. The method of claim 2, wherein the candidate opinion data comprises a plurality of candidate opinion data; the determining, from at least one candidate content, a content to be recommended associated with the candidate comment data includes:
classifying the candidate comment data to obtain a plurality of categories;
determining a target category from a plurality of categories based on the number of candidate review data in each category; and
and determining content to be recommended from at least one candidate content based on the target category, wherein the content to be recommended is associated with candidate comment data in the target category.
4. The method of claim 3, wherein the determining the content to be recommended from at least one candidate content based on the target category comprises:
extracting key information from the candidate comment data of the target category; and
and selecting candidate content containing the key information from the at least one candidate content as the content to be recommended.
5. The method according to any one of claims 1-4, wherein the target video segment having a degree of attention satisfying a preset degree of attention condition comprises at least one of:
the number of the comment data for the target video clip is greater than a preset number; and
the number of comment data for the target video segment is greater than the number of comment data for remaining video segments, wherein the remaining video segments are video segments of the target video other than the target video segment.
6. The method according to any one of claims 1 to 5, wherein the content to be recommended comprises advertising content.
7. A content recommendation apparatus comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring comment data aiming at a target video, and the comment data is used for representing the attention of the target video;
a first determining module, configured to determine a target video segment from the target video based on the comment data, where a degree of attention of the target video segment satisfies a preset degree of attention condition;
a second determination module, configured to determine, from at least one candidate content, a content to be recommended that is associated with the comment data; and
and the recommending module is used for recommending the content to be recommended in response to the fact that the target video is detected to be played to the target video segment.
8. The apparatus of claim 7, wherein the comment data includes a plurality of comment data; the second determining module includes:
a first determination sub-module configured to determine candidate comment data from the plurality of comment data, wherein the candidate comment data is data for the target video segment; and
and the second determining submodule is used for determining the content to be recommended associated with the candidate comment data from at least one candidate content.
9. The apparatus of claim 8, wherein the candidate opinion data comprises a plurality of candidate opinion data; the second determination submodule includes:
the classification unit is used for classifying the candidate comment data to obtain a plurality of classes;
a first determination unit configured to determine a target category from a plurality of categories based on the number of candidate comment data in each category; and
and the second determining unit is used for determining the content to be recommended from at least one candidate content based on the target category, wherein the content to be recommended is associated with the candidate comment data in the target category.
10. The apparatus of claim 9, wherein the second determining unit comprises:
the extracting subunit is used for extracting key information from the candidate comment data of the target category; and
and the selecting subunit is used for selecting the candidate content containing the key information from the at least one candidate content as the content to be recommended.
11. The apparatus according to any one of claims 7-10, wherein the target video segment having a degree of attention satisfying a preset degree of attention condition comprises at least one of:
the number of the comment data for the target video clip is greater than a preset number; and
the number of comment data for the target video segment is greater than the number of comment data for remaining video segments, wherein the remaining video segments are video segments of the target video other than the target video segment.
12. The apparatus according to any one of claims 7-11, wherein the content to be recommended includes advertisement content.
13. 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 1-6.
14. 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.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.
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