CN114866818A - Video recommendation method and device, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the application belongs to the field of artificial intelligence and relates to a video recommendation method, a video recommendation device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring video browsing behavior data of a user; generating user demand evaluation information according to the video browsing behavior data, wherein the user demand evaluation information comprises user demand questions, demand evaluation values corresponding to the user demand questions and answer definition; when video recommendation needs to be carried out on a user according to the user demand evaluation information, calculating a matching value of the user demand information and the video tags of the candidate videos; and selecting the candidate video as a recommended video according to the matching value, and pushing the recommended video to the user. In addition, the application also relates to a block chain technology, and the video browsing behavior data can be stored in the block chain. The method and the device improve the accuracy of video recommendation.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a video recommendation method and apparatus, a computer device, and a storage medium.
Background
With the development of computer and internet technologies, live and short videos have also been rapidly developed. The media form of live broadcast and short video can make users easily obtain information, so that the service side often utilizes the live broadcast and short video to carry out product publicity and recommendation.
Live broadcast is also a kind of video, and a service party generally recommends a video related to a product to a user, so as to promote and recommend the product. However, current video recommendation techniques generally make recommendations based on the user's browsing habits. Some types of commodities are complex, the information amount is large, the information needed to be known by a user is large, video recommendation is performed according to browsing habits, and videos needed by the user are often difficult to recommend.
Disclosure of Invention
An embodiment of the application aims to provide a video recommendation method, a video recommendation device, a computer device and a storage medium, so as to solve the problem of low video recommendation accuracy.
In order to solve the above technical problem, an embodiment of the present application provides a video recommendation method, which adopts the following technical solutions:
acquiring video browsing behavior data of a user;
generating user demand evaluation information according to the video browsing behavior data, wherein the user demand evaluation information comprises user demand questions, demand evaluation values corresponding to the user demand questions and answer definition;
when the video recommendation of the user is determined to be needed according to the user demand evaluation information, calculating a matching value of the user demand information and the video tags of the candidate videos;
and selecting a candidate video as a recommended video according to the matching value, and pushing the recommended video to the user.
In order to solve the above technical problem, an embodiment of the present application further provides a video recommendation apparatus, which adopts the following technical solutions:
the data acquisition module is used for acquiring video browsing behavior data of a user;
the evaluation generation module is used for generating user demand evaluation information according to the video browsing behavior data, wherein the user demand evaluation information comprises a user demand question, a demand evaluation value corresponding to the user demand question and answer definition;
the matching calculation module is used for calculating the matching value of the user demand information and the video label of each candidate video when the user demand evaluation information determines that video recommendation needs to be carried out on the user;
and the video pushing module is used for selecting a candidate video as a recommended video according to the matching value and pushing the recommended video to the user.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
acquiring video browsing behavior data of a user;
generating user demand evaluation information according to the video browsing behavior data, wherein the user demand evaluation information comprises user demand questions, demand evaluation values corresponding to the user demand questions and answer definition;
when the video recommendation of the user is determined to be needed according to the user demand evaluation information, calculating a matching value of the user demand information and the video tags of the candidate videos;
and selecting a candidate video as a recommended video according to the matching value, and pushing the recommended video to the user.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
acquiring video browsing behavior data of a user;
generating user demand evaluation information according to the video browsing behavior data, wherein the user demand evaluation information comprises user demand questions, demand evaluation values corresponding to the user demand questions and answer definition;
when the video recommendation of the user is determined to be needed according to the user demand evaluation information, calculating a matching value of the user demand information and the video tags of the candidate videos;
and selecting a candidate video as a recommended video according to the matching value, and pushing the recommended video to the user.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects: the method comprises the steps of obtaining video browsing behavior data, recording various behaviors of a user when the user watches videos through the video browsing behavior data, analyzing the behaviors of the user according to the video browsing behavior data, and generating user requirement evaluation information; the user requirement evaluation information comprises a user requirement question, a requirement evaluation value and answer definition corresponding to the user requirement question, the requirement evaluation value indicates the interest degree of the user to the question, and the answer definition indicates the understanding degree of the user to the answer of the question; according to the user demand evaluation information, user demands can be analyzed, and whether video recommendation needs to be carried out on the user is determined; if the candidate videos are needed, the matching values of the user demand information and the video tags of the candidate videos are calculated, the relevance between the user demand problem and the candidate videos is indicated by the matching values, the candidate videos relevant to the user demand problem are selected according to the matching values and pushed to the user, accurate pushing according to the user demand is achieved, and accuracy of video recommendation is improved.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a video recommendation method according to the present application;
FIG. 3 is a schematic block diagram of one embodiment of a video recommendation device according to the present application;
FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is the medium used to provide communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture experts Group Audio Layer iv, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the video recommendation method provided in the embodiment of the present application is generally executed by a server, and accordingly, the video recommendation apparatus is generally disposed in the server.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continuing reference to FIG. 2, a flow diagram of one embodiment of a video recommendation method in accordance with the present application is shown. The video recommendation method comprises the following steps:
step S201, acquiring video browsing behavior data of a user.
In this embodiment, an electronic device (for example, a server shown in fig. 1) on which the video recommendation method operates may communicate with the terminal through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G/5G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
Specifically, the method and the device for recommending the video first acquire video browsing behavior data and recommend the video based on the video browsing behavior data. The video browsing behavior data can be behavior data left by a user in browsing videos (including live videos and short videos) on a certain platform; the video browsing behavior data may also include video recording information of videos browsed by the user, and the video recording information records information related to the videos, such as video tags, video contents and the like.
It is emphasized that, in order to further ensure the privacy and security of the video browsing behavior data, the video browsing behavior data may also be stored in a node of a blockchain.
The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a string of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, which is used for verifying the validity (anti-counterfeiting) of the information and generating a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Step S202, generating user requirement evaluation information according to the video browsing behavior data, wherein the user requirement evaluation information comprises user requirement questions, requirement evaluation values corresponding to the user requirement questions and answer definition.
Specifically, the video browsing behavior data records the behavior related to the user browsing the video, for example, the problem that the user presents when watching a live video or a short video, and the problem that the user presents is the expression of a certain requirement (for example, the requirement on the video, and the requirement on a video-related product) of the user, so that the user requirement problem can be extracted.
Meanwhile, the demand evaluation value and the answer definition can be calculated according to the video browsing behavior data. The demand evaluation value evaluates the degree of demand of the user, and the demand evaluation value is higher when it is evaluated that the user is more interested in the posed question. The answer clarity is used for evaluating whether the answer of the user to the question required by the user is clear, and the answer clarity is higher when the answer of the user to the question is evaluated to be clearer.
The user demand question, the demand evaluation value, and the answer clarity are user demand evaluation information extracted from the video browsing behavior data.
Further, the step S202 may include: extracting user demand problems based on user search information and user comment information in the video browsing behavior data; calculating a demand evaluation value and answer definition corresponding to a user demand question according to time information and video recording information corresponding to user search information and user comment information; and determining the user requirement question, the requirement evaluation value and the answer definition as the user requirement evaluation information.
Specifically, the video browsing behavior data records user search information and user comment information of the user on the video platform. The user search information may be information left after the user searches on the video platform, for example, the user may search on the video platform "how much the XX automobile is consumed by the XX automobile" and "how much the XX insurance product is paid by the XX insurance product", which may be the user search information. The user comment information may be comment information left by the user while watching the video, for example, when the user watches a live broadcast of an insurance product, a comment "XX insurance product premium is left". According to the user search information and the user comment information, the user requirement problem can be extracted.
The user search information and the user comment information also have corresponding time information, for example, each time a user leaves a comment on the video platform, the corresponding time may be recorded. When it is determined that the user generates the same user demand question multiple times based on the time information, it indicates that the user has tried to obtain an answer for one question multiple times, it may be determined that the user has a high demand for the question, and a high demand evaluation value may be added. The video on the video platform has video recording information, the video recording information can record the content of the video in different time periods, and whether the user obtains answer information related to the problem required by the user through the video can be judged according to the time information and the video recording information of the problem provided by the user, so that whether the answer of the user to the problem is clear or not is judged, and the definition of the answer can be provided.
The user requirement question, the requirement evaluation value and the answer definition are user requirement evaluation information extracted from the video browsing behavior data.
In one embodiment, when a user watches a video, the user can approve or collect the video comments, and when the video comments contain questions, the video comments can also be extracted as user requirement questions. The user demand question extracted based on the like behavior or the favorite behavior may also give a demand evaluation value and answer clarity.
In one embodiment, the demand evaluation value and the answer clarity can be determined according to preset rules, for example, the demand evaluation value is given according to the number of times that the user proposes the user demand question; alternatively, the video browsing behavior data can be input into the neural network model to obtain the demand evaluation value and the answer definition.
In the embodiment, the user demand problem is determined according to the user search information and the user comment information, and the demand evaluation value and the answer clarity corresponding to the user demand problem are determined according to the time information and the video recording information, so that the user demand evaluation information for evaluating and analyzing the user demand is obtained.
Step S203, when the video recommendation to the user is determined to be needed according to the user requirement evaluation information, calculating the matching value of the user requirement information and the video label of each candidate video.
Specifically, for the user demand question in the user demand evaluation information, if the demand evaluation value is high and the answer clarity is low, video recommendation can be performed to the user to help the user to answer the user demand question through the recommended video.
The video platform pre-stores a plurality of candidate videos, and the candidate videos are provided with video tags, wherein the video tags are used for describing the candidate videos, such as describing the content, the type and the like of the candidate videos.
The matching value of the user requirement information and each candidate video can be calculated, and the matching value represents the relevance of the user requirement problem in the user requirement information and each candidate video in a digital mode.
Further, the step of calculating the matching value between the user requirement information and the video tag of each candidate video may include: acquiring a video label of each candidate video; respectively performing word segmentation processing on a user demand problem and a video label in user demand information to obtain a plurality of sub-words; and calculating the matching value of the user demand information and each video label according to the obtained sub-words.
Specifically, video tags of all candidate videos are obtained, the video tags are different in length and can be longer or shorter, and word segmentation processing can be performed on the video tags to obtain a plurality of sub-words; and then, performing word segmentation on the user requirement problem to obtain a plurality of sub-words. And calculating the matching value of the user requirement problem in the user requirement information and each video label according to the obtained sub-words.
In one embodiment, the match value is expressed as:
wherein A is w To the problem of user demand, B w For video tags, DP (A) w ∩B w ) The concept of representing the user requirement problem and the degree of polymerization of the video label, which originally comes from the chemical field, is an index for measuring the molecular size of the polymer. The number of the repeating units is taken as a reference, namely the average value of the number of the repeating units contained in the macromolecular chain of the polymer is expressed by n; based on the number of structural units, i.e., the number of individual structural units contained in the macromolecular chain of the polymer. In this application, the degree of polymerization may be an average of the number of identical or similar sub-words.
Indicating the number of identical sub-words in the user demand question and the video tag,
indicating the user demand question and the number of all subwords in the video label.
In one embodiment, the user requirement question and the video tag can be converted into vectors, and then the cosine similarity between the vectors is calculated to serve as a matching value of the user requirement question and the video tag.
In this embodiment, the video tag and the user requirement problem are segmented to obtain a plurality of words, and the matching value is calculated according to the sub-words, so that the matching degree between the user requirement problem and each candidate video is obtained.
Further, the step of obtaining the video tag of each candidate video includes: for each candidate video, acquiring a short label added in advance by the candidate video; acquiring video voice of the candidate video, and performing voice recognition on the video voice to obtain a voice recognition result; acquiring a video screenshot of a candidate video, and performing image recognition on the video screenshot to obtain an image recognition result; constructing a long label of a candidate video according to the voice recognition result and the image recognition result; and generating video labels of the candidate videos according to the short labels and the long labels.
Specifically, the video tags of each candidate video include a short tag and a long tag, the short tag may describe the candidate video in a shorter text, and the long tag may describe the candidate video in a longer text.
The short label can be added in advance, for example, when a user uploads a video to a video platform, the short label of the video can be set by himself, or the video platform identifies the video and adds the short label.
The candidate video has video voice, and voice recognition is carried out on the video voice to obtain a voice recognition result; and screenshot can be carried out on the candidate video to obtain a video screenshot, the video screenshot can contain explanatory information such as subtitles, image recognition is carried out on the video screenshot, and an image recognition result is obtained. The voice recognition result and the image recognition result are detailed descriptions of the video content of the candidate video, and the long label can be constructed according to the voice recognition result and the image recognition result, and then the short label and the long label are spliced together to obtain the video label of the candidate video.
In the embodiment, the short tags of the candidate videos are obtained, then the voice recognition is performed on the video voices of the candidate videos, the image recognition is performed on the video screenshots, the long videos are constructed according to the video results, and therefore the video tags for describing the candidate videos in detail are obtained according to the short tags and the long tags.
And S204, selecting the candidate video as a recommended video according to the matching value, and pushing the recommended video to the user.
Specifically, a candidate video with a higher matching value is selected as a recommended video, for example, the calculated matching value is compared with a preset matching threshold, and the candidate video with the matching value greater than the matching threshold is used as the recommended video; or sorting the matching values from large to small, and selecting the candidate video with the top N bits as the recommended video.
And when the fact that the user logs in the video platform is detected, recommending the recommended video to the user, and therefore video recommendation is completed. For example, because the function of the product to be recommended is complex and more information needs to be introduced, based on the current short video production mode, a plurality of different short videos can be produced, and the product is introduced from different layers. After a user browses some short videos or live videos, video browsing behavior data is generated, the amount of the insurance premium of the XX insurance in a user demand question is extracted, then a matching value is calculated according to the demand question and the video tags of candidate videos, the short videos related to the XX insurance premium are introduced in a special way are selected, and then the short videos are recommended to the user, so that the doubt of the user is solved. The product to be recommended may be various products, and in one embodiment, the product to be recommended may be a product in the financial, insurance field.
In an embodiment, when the calculated matching values are all smaller than the preset matching threshold, it is indicated that there is no candidate video matching with the user requirement problem, and operation instruction information may be generated according to the user requirement problem and pushed to relevant staff to remind the staff of making a new video.
In the embodiment, video browsing behavior data is acquired, the video browsing behavior data records various behaviors of a user when the user watches videos, and the user behaviors can be analyzed according to the video browsing behavior data to generate user demand evaluation information; the user requirement evaluation information comprises a user requirement question, a requirement evaluation value and answer definition corresponding to the user requirement question, the requirement evaluation value indicates the interest degree of the user to the question, and the answer definition indicates the understanding degree of the user to the answer of the question; according to the user demand evaluation information, user demands can be analyzed, and whether video recommendation needs to be carried out on the user is determined; if the candidate videos are needed, the matching values of the user demand information and the video tags of the candidate videos are calculated, the relevance between the user demand problem and the candidate videos is indicated by the matching values, the candidate videos relevant to the user demand problem are selected according to the matching values and pushed to the user, accurate pushing according to the user demand is achieved, and accuracy of video recommendation is improved.
Further, after the step S204, the method may further include: acquiring push feedback data of a user for a recommended video; updating user demand evaluation information of the user based on the push feedback data; determining the user type of the user according to the user requirement evaluation information; and when the user belongs to the preset user type, pushing the candidate product corresponding to the recommended video to the user.
Specifically, after a recommendation video related to the user demand problem is pushed to the user, the user can watch the recommendation video, and behavior data generated in the process is recorded as push feedback data.
The user demand assessment information can be updated according to the push feedback data. For example, when it is detected that the user has viewed the recommended video, the resolution of the answer in the user demand evaluation information may be increased. Meanwhile, the purchase evaluation value of the user on the product can be determined according to the user demand evaluation information, the purchase evaluation value measures the possible purchase degree of the user on the product, and the user type can be the classification of the user according to the purchase evaluation value. When the user type belongs to a preset user type (namely the user type with higher possibility of purchasing the product), pushing the candidate product corresponding to the recommended video to the user; for example, a purchase page of the candidate product is pushed to the user.
In one embodiment, the purchase evaluation value of the user may be determined by a preset rule based on the user demand evaluation information, for example, the purchase evaluation value is calculated according to the demand evaluation value and the answer clarity, and the higher the demand evaluation value is and the higher the answer clarity is, the higher the purchase evaluation value is. Or inputting the user requirement evaluation information or the video browsing behavior data into a model built based on a neural network, and outputting the purchase evaluation value of the user through the model.
In the embodiment, the user demand evaluation information is updated according to the push feedback information, and when the candidate product is determined to have high possibility of being purchased by the user according to the user demand evaluation information, the candidate product is pushed to the user, so that product recommendation based on user behavior analysis is realized, and the accuracy of product recommendation is improved.
Further, after the step of obtaining the push feedback data of the user for the recommended video, the method may further include: acquiring the broadcasting completion position of the recommended video from the push feedback data; acquiring an answer position of a recommended video; when the playing position is smaller than the answer position, the pushing of the recommended video is marked as abnormal pushing; and when the pushing quantity of the abnormal pushing is detected to be larger than a preset quantity threshold value, generating video early warning information of the recommended video.
Specifically, when the user watches the recommended video, the user may quit the video watching at a certain time point, for example, quit the video watching after the video watching is finished, or quit the video watching in the middle of the video, and a position where the user quits the video watching is an end playing position.
And recommending answer information of the user-required questions in the video, wherein the positions of the answer information are answer positions. In one embodiment, the end play position and the answer position may be time information, for example, the time information includes minutes and seconds, thereby locating the end play position and the answer position. And comparing the sizes of the playing position and the answer position, if the playing position is smaller than the answer position, indicating that the user quits the recommended video watching before watching the answer information, wherein at the moment, the video pushing behavior does not obtain a substantial effect, and the pushing is marked as abnormal pushing.
If the pushed quantity of the abnormal push generated by the recommended video is detected to be larger than the preset quantity threshold value, the recommended video is low in effect on solving the user demand problem, and video early warning information can be generated. The video early warning information is sent to relevant workers, so that the relevant workers can reproduce videos to improve the video quality.
In this embodiment, when the playing completion position is smaller than the answer position, the push is marked as abnormal push; when the pushing quantity of the abnormal pushing is larger than the preset quantity threshold value, video early warning information indicating poor video quality is generated so as to adjust the video in time and improve the probability of obtaining answer information through the video by a user.
Further, the video recommendation method may further include: and when the video recommendation is determined not to be required to be performed on the user according to the user requirement evaluation information, or when the user is detected to be a risk user, stopping the video recommendation process.
Specifically, when the user is determined to have no interest in the candidate product related to the video according to the user requirement evaluation information or the user knows the problem related to the product, the video recommendation process can be stopped without performing video recommendation on the user; alternatively, the video recommendation process may also be stopped when it is detected that the user is a risky user. The risky user may be a user who detects that the account of the user has abnormal use, or the user is a blacklisted user.
In this embodiment, when it is determined that video recommendation is not required for the user according to the user demand evaluation information, or when it is detected that the user is a risk user, the video recommendation process is stopped, so as to reduce consumption of computing resources.
In one embodiment, the server stores the user demand assessment information via three storage areas, e.g., three tables. The first table is used for storing user demand questions and related information thereof, and comprises user identification (namely user ID which is used for uniquely identifying a certain user) for raising questions, demand evaluation value, live question raising time (time point when the user raises the user demand questions in live broadcasting), live question answering time (time point when the live broadcasting is used for identifying whether the live broadcasting is used for answering the user demand questions), live question answering time (time point when the live broadcasting is used for answering the user demand questions), video identification (video ID) of recommended videos, answer positions (positions of answer information in the recommended videos), play finishing positions of the recommended videos, answers obtained through the recommended videos, answer definition, purchase evaluation value and video recommendation process ending. The "whether to obtain an answer by recommending a video", and "whether to end a video recommendation process" may be stored in a form of 0 to 1, and the remaining fields may be stored in a form of percentage values. The information in the first storage area may be from video browsing behavior data as well as push feedback data.
The second storage area is used for storing the fluctuation factor of each field in the first storage area, and the field in the first storage area can increase or decrease the value according to the fluctuation factor. The fluctuation factor may be set manually according to the traffic requirements, for example, the fluctuation factor may be set to 10%.
The third storage area is used for storing the generated operation indication information. The method and the device can analyze the video browsing behavior data, for example, the video browsing behavior data is input into a model built based on a neural network for analysis, and the operation abnormity is recorded; for example, according to the matching value, a recommended video cannot be matched, or the user has a high demand evaluation value, the anchor solves the user demand problem in the live broadcast, but the user still has frequent search behavior or question asking behavior for the user demand. The operation indication information is sent to relevant staff for analysis.
The demand evaluation value can be obtained according to a user portrait, wherein the user portrait comprises question search times, question asking times and search information of each time after the user watches the video, and the question search times and the question asking times are extracted from the video browsing behavior data; wherein, the more the number of searches and questions, the more the value of the demand evaluation value is increased according to the fluctuation factor. If the time interval of the user questions before the user asks the main broadcasting to solve the question is longer in the video watching process, the demand evaluation value is also larger; the larger the demand evaluation value is, the higher the priority of performing the corresponding video push is. Meanwhile, the demand estimation value is reduced by a certain percentage at intervals as time goes by. In the live broadcast, if the main broadcast solves the problem, and the demand evaluation value of a plurality of users on a certain user demand problem is still high, the running constant record is generated, and the display data is wrongly recorded in a third storage area.
In live broadcasting, video voice can be converted into characters, and the converted characters are matched with user requirement problems. And if the answer is determined to be answered by the anchor in the live broadcast after matching, determining whether the answer is answered in the live broadcast and corresponding numerical values of the answer time in the live broadcast. Live question-asking time, live answer-answering and live answer-answering time can be used for determining the demand evaluation value and the answer definition.
After the first storage area is updated according to the push feedback data, if the playing position is smaller than the answer position, the user does not see the answer in the recommended video, and the answer definition cannot be improved. The video platform can continuously push the recommended videos containing the answer information, if the number of the clients is large, video early warning information is generated, the quality of the problems is improved, or the answer positions are advanced.
The answer definition can be calculated according to the playing completion position and the demand evaluation value, the playing completion position value is higher, and the demand evaluation value is gradually reduced along with the time, so that the user definition of the answer is higher.
The purchase evaluation value may be analyzed based on the demand evaluation value, the answer clarity, and the user profile of the user such as the likes, the favorites, the comments, the viewing time of the video, and may be gradually decreased as time passes.
When the user does not watch the video for a period of time after receiving the answer, setting whether to end the video recommendation process as 1; alternatively, when the video platform detects that the user is a risky client, the field value is set to 1.
The data in the three storage areas can asynchronously and independently output a service for operation after the watching is finished; in order to ensure the operation efficiency, a certain amount of clients can be operated by using multiple threads for the first time, and the subsequent updating data can be automatically calculated and updated in a preset time (for example, the time when the number of visitors is small).
According to the method and the device, whether the user comment information is effective or not can be identified through natural language processing, the user demand problem is detected, and whether the user puts forward the same user demand problem at other time points or not is identified. If the user has presented the same user demand question at other points in time and has not acquired answer information, the answer clarity is reduced. The video platform can search users with the same condition, and if the number of the users is large, operation indication information is generated to prompt that the video quality containing corresponding answers is insufficient, replacement is recommended, or the information transmitted by the anchor is not clear.
In the live broadcast, if the anchor answers the user requirement question, the user responds to the answer, continuously watches the live broadcast, the requirement evaluation value rises, the purchase evaluation value also rises, and the answer definition also rises.
And if the fact that the user does not respond to the solution of the anchor in the live broadcast is identified, or the anchor does not solve the problem of the user and the demand evaluation value continues to rise, the resolution of the answer is unchanged, and the relevant video is recommended to the user according to the video label.
And if the related candidate video cannot be found according to the matching value, generating operation indication information, prompting that the video related to the user requirement problem cannot be found, and recommending to update the video.
If the playing completion position is larger than the answer position, increasing the answer definition according to the fluctuation factor; and if the playing position is smaller than the answer position, the answer definition is not improved, the demand evaluation value continues to rise, the video push is marked as abnormal push, and when the fact that the number of the abnormal push is larger than the preset number threshold value is detected, video early warning information is generated and stored in a third storage area.
In addition, whether the user pays attention to the user demand problem can be judged through data such as the return browsing volume, the video replay frequency and the replay position (the watching position when the user watches the video again) of the user, and if so, the demand evaluation value is improved, and the purchase evaluation value is improved.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a video recommendation apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied in various electronic devices.
As shown in fig. 3, the video recommendation apparatus 300 according to the present embodiment includes: a data acquisition module 301, an evaluation generation module 302, a matching calculation module 303, and a video push module 304, wherein:
a data obtaining module 301, configured to obtain video browsing behavior data of a user.
The evaluation generating module 302 is configured to generate user requirement evaluation information according to the video browsing behavior data, where the user requirement evaluation information includes a user requirement question, a requirement evaluation value corresponding to the user requirement question, and a resolution of an answer.
And the matching calculation module 303 is configured to calculate a matching value between the user demand information and the video tag of each candidate video when it is determined that video recommendation needs to be performed on the user according to the user demand evaluation information.
And the video pushing module 304 is configured to select a candidate video as a recommended video according to the matching value, and push the recommended video to the user.
In the embodiment, video browsing behavior data is obtained, the video browsing behavior data records various behaviors of a user when the user watches videos, and the user behaviors can be analyzed according to the video browsing behavior data to generate user demand evaluation information; the user requirement evaluation information comprises a user requirement question, a requirement evaluation value and answer definition corresponding to the user requirement question, the requirement evaluation value indicates the interest degree of the user to the question, and the answer definition indicates the understanding degree of the user to the answer of the question; according to the user demand evaluation information, user demands can be analyzed, and whether video recommendation needs to be carried out on the user is determined; if the candidate videos are needed, the matching values of the user demand information and the video tags of the candidate videos are calculated, the relevance between the user demand problem and the candidate videos is indicated by the matching values, the candidate videos relevant to the user demand problem are selected according to the matching values and pushed to the user, accurate pushing according to the user demand is achieved, and accuracy of video recommendation is improved.
In some optional implementations of this embodiment, the evaluation generation module 302 may include: the problem extraction submodule, the calculation submodule and the evaluation determination submodule, wherein:
and the problem extraction submodule is used for extracting the user requirement problem based on the user search information and the user comment information in the video browsing behavior data.
And the calculating submodule is used for calculating the demand evaluation value and the answer definition corresponding to the user demand problem according to the time information and the video recording information corresponding to the user searching information and the user comment information.
And the evaluation determining sub-module is used for determining the user requirement question, the requirement evaluation value and the answer definition as the user requirement evaluation information.
In the embodiment, the user demand problem is determined according to the user search information and the user comment information, and the demand evaluation value and the answer clarity corresponding to the user demand problem are determined according to the time information and the video recording information, so that the user demand evaluation information for evaluating and analyzing the user demand is obtained.
In some optional implementations of this embodiment, the matching calculation module 303 may include: the tag acquisition submodule, the word segmentation submodule and the matching calculation submodule, wherein:
and the label obtaining sub-module is used for obtaining the video labels of the candidate videos.
And the word segmentation sub-module is used for performing word segmentation processing on the user requirement problem and the video label in the user requirement information respectively to obtain a plurality of sub-words.
And the matching calculation submodule is used for calculating the matching value of the user demand information and each video label according to the obtained sub-words.
In this embodiment, the video tag and the user requirement problem are segmented to obtain a plurality of words, and the matching value is calculated according to the sub-words, so that the matching degree between the user requirement problem and each candidate video is obtained.
In some optional implementations of this embodiment, the tag obtaining sub-module may include: short label acquisition unit, speech recognition unit, image recognition unit, long label construction unit and label generate the unit, wherein:
and the short label acquisition unit is used for acquiring the short label added in advance by the candidate video for each candidate video.
And the voice recognition unit is used for acquiring the video voice of the candidate video and performing voice recognition on the video voice to obtain a voice recognition result.
And the image recognition unit is used for acquiring the video screenshot of the candidate video, and performing image recognition on the video screenshot to obtain an image recognition result.
And the long label constructing unit is used for constructing a long label of the candidate video according to the voice recognition result and the image recognition result.
And the label generating unit is used for generating the video label of the candidate video according to the short label and the long label.
In the embodiment, the short tags of the candidate videos are obtained, then the voice recognition is performed on the video voices of the candidate videos, the image recognition is performed on the video screenshots, the long videos are constructed according to the video results, and therefore the video tags for describing the candidate videos in detail are obtained according to the short tags and the long tags.
In some optional implementations of this embodiment, the video recommendation apparatus 300 may further include: feedback acquisition module, aassessment update module, type confirm module and product push module, wherein:
and the feedback acquisition module is used for acquiring push feedback data of the user aiming at the recommended video.
And the evaluation updating module is used for updating the user requirement evaluation information of the user based on the push feedback data.
And the type determining module is used for determining the user type of the user according to the user requirement evaluation information.
And the product pushing module is used for pushing the candidate products corresponding to the recommended videos to the user when the user belongs to the preset user type.
In the embodiment, the user demand evaluation information is updated according to the push feedback information, and when the candidate product is determined to have high possibility of being purchased by the user according to the user demand evaluation information, the candidate product is pushed to the user, so that product recommendation based on user behavior analysis is realized, and the accuracy of product recommendation is improved.
In some optional implementations of this embodiment, the video recommendation apparatus 300 may further include: the broadcasting completion position acquisition module, the answer position acquisition module, the abnormal pushing marking module and the early warning information generation module are arranged, wherein:
and the broadcasting completion position acquisition module is used for acquiring the broadcasting completion position of the recommended video from the push feedback data.
And the answer position acquisition module is used for acquiring the answer position of the recommended video.
And the abnormal pushing marking module is used for marking the pushing of the recommended video as abnormal pushing when the playing completion position is smaller than the answer position.
And the early warning information generation module is used for generating video early warning information of the recommended video when the pushing quantity detected to be abnormally pushed is larger than a preset quantity threshold value.
In this embodiment, when the playing completion position is smaller than the answer position, the push is marked as abnormal push; when the pushing quantity of the abnormal pushing is larger than the preset quantity threshold value, video early warning information indicating poor video quality is generated so as to adjust the video in time and improve the probability of obtaining answer information through the video by a user.
In some optional implementations of this embodiment, the video recommendation apparatus 300 may further include: and the process stopping module is used for stopping the video recommending process when the video recommending to the user is determined not to be needed according to the user requirement evaluating information or when the user is detected to be a risk user.
In this embodiment, when it is determined that video recommendation does not need to be performed on the user according to the user demand evaluation information, or when it is detected that the user is a risk user, the video recommendation process is stopped, so as to reduce consumption of computing resources.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It is noted that only computer device 4 having components 41-43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user in a keyboard mode, a mouse mode, a remote controller mode, a touch panel mode or a voice control equipment mode.
The memory 41 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 4. Of course, the memory 41 may also include both internal and external storage devices of the computer device 4. In this embodiment, the memory 41 is generally used for storing an operating system installed in the computer device 4 and various application software, such as computer readable instructions of a video recommendation method. Further, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the video recommendation method.
The network interface 43 may comprise a wireless network interface or a wired network interface, and the network interface 43 is generally used for establishing communication connection between the computer device 4 and other electronic devices.
The computer device provided in this embodiment may execute the video recommendation method. The video recommendation method here may be the video recommendation method of the above-described embodiments.
In the embodiment, video browsing behavior data is acquired, the video browsing behavior data records various behaviors of a user when the user watches videos, and the user behaviors can be analyzed according to the video browsing behavior data to generate user demand evaluation information; the user requirement evaluation information comprises a user requirement question, a requirement evaluation value and answer definition corresponding to the user requirement question, the requirement evaluation value indicates the interest degree of the user to the question, and the answer definition indicates the understanding degree of the user to the answer of the question; according to the user demand evaluation information, user demands can be analyzed, and whether video recommendation needs to be carried out on the user is determined; if the candidate videos are needed, the matching values of the user demand information and the video tags of the candidate videos are calculated, the relevance between the user demand problem and the candidate videos is indicated by the matching values, the candidate videos relevant to the user demand problem are selected according to the matching values and pushed to the user, accurate pushing according to the user demand is achieved, and accuracy of video recommendation is improved.
The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the video recommendation method as described above.
In the embodiment, video browsing behavior data is acquired, the video browsing behavior data records various behaviors of a user when the user watches videos, and the user behaviors can be analyzed according to the video browsing behavior data to generate user demand evaluation information; the user requirement evaluation information comprises a user requirement question, a requirement evaluation value and answer definition corresponding to the user requirement question, the requirement evaluation value indicates the interest degree of the user to the question, and the answer definition indicates the understanding degree of the user to the answer of the question; according to the user demand evaluation information, user demands can be analyzed, and whether video recommendation needs to be carried out on the user is determined; if the candidate videos are needed, the matching values of the user demand information and the video tags of the candidate videos are calculated, the relevance between the user demand problem and the candidate videos is indicated by the matching values, the candidate videos relevant to the user demand problem are selected according to the matching values and pushed to the user, accurate pushing according to the user demand is achieved, and accuracy of video recommendation is improved.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A method for video recommendation, comprising the steps of:
acquiring video browsing behavior data of a user;
generating user demand evaluation information according to the video browsing behavior data, wherein the user demand evaluation information comprises user demand questions, demand evaluation values corresponding to the user demand questions and answer definition;
when the video recommendation of the user is determined to be needed according to the user demand evaluation information, calculating a matching value of the user demand information and the video tags of the candidate videos;
and selecting a candidate video as a recommended video according to the matching value, and pushing the recommended video to the user.
2. The video recommendation method according to claim 1, wherein said step of generating user demand evaluation information based on said video browsing behavior data comprises:
extracting user demand problems based on user search information and user comment information in the video browsing behavior data;
calculating a demand evaluation value and answer definition corresponding to the user demand question according to time information and video recording information corresponding to the user search information and the user comment information;
and determining the user requirement question, the requirement evaluation value and the answer definition as user requirement evaluation information.
3. The video recommendation method according to claim 1, wherein the step of calculating the matching value of the user requirement information and the video tag of each candidate video comprises:
acquiring a video label of each candidate video;
performing word segmentation processing on the user requirement problem in the user requirement information and the video label respectively to obtain a plurality of sub-words;
and calculating the matching value of the user demand information and each video label according to the obtained sub-words.
4. The video recommendation method according to claim 1, wherein the step of obtaining the video tag of each candidate video comprises:
for each candidate video, acquiring a short label added in advance by the candidate video;
acquiring video voice of the candidate video, and performing voice recognition on the video voice to obtain a voice recognition result;
acquiring a video screenshot of the candidate video, and performing image recognition on the video screenshot to obtain an image recognition result;
constructing a long label of the candidate video according to the voice recognition result and the image recognition result;
and generating the video label of the candidate video according to the short label and the long label.
5. The video recommendation method according to claim 1, wherein after the step of selecting a candidate video as a recommended video according to the matching value and pushing the recommended video to the user, the method further comprises:
acquiring push feedback data of a user aiming at the recommended video;
updating user demand evaluation information of the user based on the push feedback data;
determining the user type of the user according to the user demand evaluation information;
and when the user belongs to a preset user type, pushing the candidate product corresponding to the recommended video to the user.
6. The video recommendation method according to claim 5, further comprising, after the step of obtaining push feedback data of the user for the recommended video:
acquiring the broadcasting completion position of the recommended video from the push feedback data;
acquiring an answer position of the recommended video;
when the playing completion position is smaller than the answer position, marking the pushing of the recommended video as abnormal pushing;
and when the pushing quantity of the abnormal pushing is detected to be larger than a preset quantity threshold value, generating video early warning information of the recommended video.
7. The video recommendation method of claim 1, further comprising:
and when the video recommendation is determined not to be required to be performed on the user according to the user demand evaluation information, or when the user is detected to be a risk user, stopping the video recommendation process.
8. A video recommendation apparatus, comprising:
the data acquisition module is used for acquiring video browsing behavior data of a user;
the evaluation generation module is used for generating user demand evaluation information according to the video browsing behavior data, wherein the user demand evaluation information comprises a user demand question, a demand evaluation value corresponding to the user demand question and answer definition;
the matching calculation module is used for calculating the matching value of the user demand information and the video label of each candidate video when the user demand evaluation information determines that video recommendation needs to be carried out on the user;
and the video pushing module is used for selecting a candidate video as a recommended video according to the matching value and pushing the recommended video to the user.
9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of the video recommendation method of any of claims 1-7.
10. A computer-readable storage medium having computer-readable instructions stored thereon which, when executed by a processor, implement the steps of the video recommendation method of any of claims 1-7.
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