Video recommendation method, apparatus, medium, and device

Abstract

The present disclosure provides a video recommendation method and device, medium and equipment, relates to the technical field of artificial intelligence, in particular to the technical field of deep learning and video recommendation. The implementation scheme is: obtaining a gyroscope parameter of a target device operated by a user within a preset time period before a current time; obtaining current state information of the user based on the gyroscope parameter; obtaining a target recommended video based on the current state information and portrait information of the user; and recommending the target recommended video to the target device.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of artificial intelligence, in particular to the technical field of deep learning and video recommendation, and specifically to a video recommendation method and device, electronic equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] Artificial intelligence is a discipline that studies enabling computers to simulate some thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.) of humans, which includes both hardware technology and software technology. Artificial intelligence hardware technology generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing, etc.; artificial intelligence software technology mainly includes computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, knowledge graph technology, etc.

[0003] With the continuous development of network video technology, accurately recommending videos that users need to users has become an important means to improve the click volume of network videos. When users see that the recommended videos are exactly what they need, they will click them without hesitation, which not only facilitates users but also benefits the development of network video platforms.

[0004] The methods described in this section do not necessarily have to be previously conceived or employed. Unless otherwise indicated, nothing in this section should be assumed to be prior art merely because of its inclusion in this section. Similarly, issues mentioned in this section should not be assumed to have been admitted to be prior art in any jurisdiction merely because of their inclusion in this section. SUMMARY

[0005] The present disclosure provides a video recommendation method, device, electronic equipment, computer readable storage medium and computer program product.

[0006] According to an aspect of the present disclosure, a video recommendation method is provided, including: obtaining a gyroscope parameter of a target device being operated by a user within a preset time period before a current time; based on the gyroscope parameter, obtaining current state information of the user, the current state information including posture information of the user when operating the target device at the current time; based on the current state information and portrait information of the user, obtaining a target recommended video; and recommending the target recommended video to the target device.

[0007] According to another aspect of the present disclosure, a video recommendation device is provided, comprising: a first obtaining unit configured to obtain a gyroscope parameter of a target device being operated by a user within a preset time period before a current time; a second obtaining unit configured to obtain current state information of the user based on the gyroscope parameter, the current state information comprising posture information of the user when operating the target device at the current time; a third obtaining unit configured to obtain a target recommended video based on the current state information and portrait information of the user; and a recommendation unit configured to recommend the target recommended video to the target device.

[0008] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above-mentioned video recommendation method.

[0009] According to another aspect of the present disclosure, a non-transitory computer readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to perform the above-mentioned video recommendation method.

[0010] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the above-mentioned video recommendation method.

[0011] According to one or more embodiments of the present disclosure, a user can be recommended a video that is more in line with a current state, and user experience is improved.

[0012] It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0013] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments and together with the description serve to explain exemplary implementations of the application. The illustrated embodiments are exemplary only and not limiting of the scope of the appended claims. In all the drawings, like reference numerals refer to like parts throughout the several views.

[0014] Figure 1 shows a schematic diagram of an exemplary system in which the various methods described herein can be implemented according to embodiments of the present disclosure;

[0015] Figure 2 shows a flowchart of a video recommendation method according to embodiments of the present disclosure;

[0016] Figure 3A flowchart of acquiring a target recommended video is shown according to an embodiment of the present disclosure;

[0017] Figure 4 A structural block diagram of a video recommendation device is shown according to an embodiment of the present disclosure;

[0018] Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to assist in understanding, which should be considered in their context only. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present disclosure. Also, in order to be clear and concise, descriptions of well-known functions and structures are omitted in the following description.

[0020] In the present disclosure, the terms "first", "second", and the like are used to describe various elements, unless otherwise stated, and are not intended to limit the positional relationship, the timing relationship or the importance relationship of the elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element can refer to the same instance of the element, and in some cases, based on the context of the description, they can also refer to different instances.

[0021] The terms used in the description of various described examples in the present disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the number of elements is specifically limited, the element can be one or more, if the number of elements is not specifically limited. In addition, the term "and / or" used in the present disclosure encompasses any one of the listed items and all possible combinations thereof.

[0022] With the rapid development of short video platforms, accurate video recommendation has become the key to improving user experience. In related technologies, video recommendation algorithms mainly rely on users' historical viewing records and basic behavior data (such as clicks, likes, comments, etc.) for video recommendation. In actual applications, the accuracy of the recommendation still needs to be improved.

[0023] The present disclosure provides a video recommendation method, by acquiring the gyroscope parameters of the target device being operated by the user, predicting the current state of the user using the target device based on the gyroscope parameters, and then comprehensively recommending videos based on the current state and the user's portrait information, so as to more accurately recommend videos that are more in line with the current state for the user, and improve the user experience.

[0024] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0025] Figure 1 A schematic diagram illustrating an example system 100 in which various methods and apparatus described herein can be implemented in accordance with embodiments of the disclosure is shown. With reference to Figure 1 The system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more application programs.

[0026] In embodiments of the disclosure, the server 120 can run one or more services or software applications that enable the video recommendation methods described above to be performed.

[0027] In certain embodiments, the server 120 can also provide other services or software applications, which can include non-virtual and virtual environments. In certain embodiments, these services can be provided as web-based services or cloud services, for example, to users of the client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0028] In Figure 1 In the illustrated configuration, the server 120 can include one or more components implementing the functionality performed by the server 120. These components can include software components, hardware components, or a combination thereof, executable by one or more processors. Users operating the client devices 101, 102, 103, 104, 105, and / or 106 can in turn utilize one or more client application programs to interact with the server 120 to utilize the services provided by these components. It will be appreciated that various different system configurations are possible, which can vary from the system 100. Accordingly, Figure 1 The system 100 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0029] A user can use the client devices 101, 102, 103, 104, 105, and / or 106 to watch recommended videos. The client devices can provide an interface that enables a user of the client device to interact with the client device. The client devices can also output information to the user via the interface. Although Figure 1 Only six client devices are depicted, but one of skill in the art will appreciate that the disclosure can support any number of client devices.

[0030] Client devices 101, 102, 103, 104, 105, and / or 106 can include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service kiosk devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and the like. These computer devices can run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux or Linux-like operating systems (such as GOOGLE Chrome OS); or including various mobile operating systems, such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices can include cellular telephones, smartphones, tablet computers, personal digital assistants (PDAs), and the like. Wearable devices can include head-mounted displays (such as smart glasses) and other devices. Gaming systems can include various handheld gaming devices, Internet-enabled gaming devices, and the like. Client devices are capable of executing a variety of different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and can use various communication protocols.

[0031] Network 110 can be any type of network familiar to those skilled in the art that can support data communications using any of a variety of available protocols, including without limitation TCP / IP, SNA, IPX, etc. As examples only, one or more of networks 110 can be a LAN, an Ethernet network, a Token Ring network, a WAN, the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a local area network (LAN), a wide area network (WAN), a wireless network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., a Bluetooth network), and / or any combination of these and / or other networks.

[0032] Server 120 can include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, midrange servers), blade servers, mainframe computers, server clusters, or any other appropriate arrangement and / or combination. Server 120 can include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (such as one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for servers). In various embodiments, server 120 can run one or more services or software applications that provide the functionality described below.

[0033] The computing units in the server 120 can run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. Server 120 can also run any of a variety of additional server applications and / or mid-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0034] In some embodiments, the server 120 can include one or more applications to analyze and consolidate data feeds and / or event updates from users of the client devices 101, 102, 103, 104, 105, and / or 106. The server 120 can also include one or more applications to display the data feeds and / or real-time events via one or more display devices of the client devices 101, 102, 103, 104, 105, and / or 106.

[0035] In some embodiments, the server 120 can be a server of a distributed system, or a server combined with a blockchain. The server 120 can also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. The cloud server is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and virtual private server (VPS, Virtual Private Server) services.

[0036] The system 100 can also include one or more databases 130. In certain embodiments, these databases can be used to store data and other information. For example, one or more of the databases 130 can be used to store information such as audio files and video files. The databases 130 can reside in a variety of locations. For example, databases used by the server 120 can reside locally to the server 120, or can be remote from the server 120 and can communicate with the server 120 via a network- or application-specific connection. The databases 130 can be of different types. In certain embodiments, databases used by the server 120 can be, for example, relational databases. One or more of these databases can store, update, and retrieve data to and from the databases in response to commands.

[0037] In certain embodiments, one or more of the databases 130 can also be used by applications to store application data. Databases used by applications can be different types of databases, such as key-value stores, object stores, or regular stores backed by file systems.

[0038] Figure 1The system 100 can be configured and operated in various ways to enable the application of various methods and apparatuses described according to the present disclosure.

[0039] According to embodiments of the present disclosure, as shown in Figure 2 A video recommendation method is provided, including: obtaining, in step S201, gyroscope parameters of a target device being operated by a user within a preset time period before a current time; obtaining, in step S202, current state information of the user based on the gyroscope parameters, the current state information including posture information of the user when operating the target device at the current time; obtaining, in step S203, a target recommended video based on the current state information and portrait information of the user; and recommending, in step S204, the target recommended video to the target device.

[0040] Thus, by obtaining the gyroscope parameters of the target device being operated by the user, predicting the current state of the user using the target device based on the gyroscope parameters, and then comprehensively recommending a video based on the current state and the portrait information of the user, a more accurate video that is more suitable for the current state can be recommended to the user, thereby improving the user experience.

[0041] In some embodiments, the target device can be a mobile phone, a tablet computer, or any other handheld terminal device, without limitation.

[0042] In some embodiments, the gyroscope parameters of the target device can be obtained at a preset time interval, and when predicting the current state information of the user, a plurality of sets of gyroscope parameters within a preset time period before the current time can be obtained.

[0043] In some embodiments, the gyroscope parameters can include, but are not limited to, angular velocity, acceleration, and device tilt angle, without limitation.

[0044] In some embodiments, the current state information of the user can represent potential behavioral characteristics of the user when watching a video, and can include posture information of the user when operating the target device at the current time, such as whether the user is holding the target device, whether the user is sitting, lying, or walking, whether the user is in a meal state, etc. It can be understood that a person skilled in the art can set a plurality of current state information according to actual conditions, without limitation.

[0045] In some embodiments, predicting the current state information of the user based on the gyroscope parameters can be implemented based on a pre-trained state prediction model. A set of gyroscope parameters within a preset time period before the current time can be input into the state prediction model to obtain the current state information of the user predicted by the model.

[0046] In some embodiments, the state prediction model can be constructed based on a neural network such as a multi-layer perception network, a Long Short-Term Memory (LSTM) network, or a Transformer network, and the sample data used to train the state prediction model can include device gyroscope parameters of a plurality of sample users when they are watching videos in a plurality of different time periods and corresponding state labels. By inputting a set of gyroscope parameters of a sample user in a time period into the state prediction model, a state prediction result output by the model is obtained, and a loss is calculated based on the state prediction result and the state label to train the model.

[0047] In some embodiments, the video recommendation based on the current state information and the user portrait information to obtain at least one target recommended video can be to input the obtained current state information and the user portrait information into a video recommendation model, and the video recommendation model analyzes the information comprehensively and recalls at least one target recommended video from a recommended video library based on the analysis result.

[0048] In some embodiments, the user portrait information can include, but is not limited to, various types of information such as gender, age, and interest of the user.

[0049] It should be particularly noted that the acquisition and use of the above-mentioned user portrait information and other user information have been authorized by the user.

[0050] In some embodiments, the above-mentioned video recommendation model can be obtained by fine-tuning training based on a large model. In some embodiments, the above-mentioned video recommendation model can also be constructed and obtained by training based on a neural network such as a Transformer network.

[0051] In some embodiments, the sample data used to train the above-mentioned video recommendation model can include state information of the sample user in the above-mentioned plurality of different time periods, the video watched, and a likeability label for the video watched, wherein the likeability label for the video watched can be determined based on a feedback operation performed by the user on the video, for example, when the user performs a negative feedback operation such as fast swiping, clicking not interested, or fast forwarding on the video, the likeability label of the video is marked as a negative label, and when the user performs a positive feedback operation such as liking, commenting, sharing, or watching all the content of the video, the likeability label of the video is marked as a positive label.

[0052] After the target recommended video is acquired, the target recommended video can be pushed to the target device of the user. In this way, by acquiring the gyroscope parameter of the target device being operated by the user, the state in which the user is currently using the target device is predicted based on the gyroscope parameter, and then video recommendation is performed by comprehensively considering the current state and the portrait information of the user, so that the user can be more accurately recommended videos that are more suitable for the current state, and user experience is improved.

[0053] In some embodiments, the acquiring of the current state information of the user based on the gyroscope parameter can include: acquiring the current state information of the user based on the gyroscope parameter and the current time.

[0054] Since there can be certain rules in the state of the user at different times (for example, usually at noon for meal time, and the state of the user has a greater probability of being a meal state), by combining the current time and the gyroscope parameter to predict the current state information, the accuracy of state prediction can be further improved.

[0055] In some embodiments, the predicting of the current state information of the user based on the gyroscope parameter and the current time can be implemented based on a pre-trained state prediction model. A set of gyroscope parameters in a preset time period before the current time and the current time (or a preset time period before the current time) can be input into the state prediction model to obtain the current state information of the user predicted by the model. It can be understood that the construction and training method of the state prediction model is similar to the construction and training method of the state prediction model described above, and will not be described here.

[0056] In some embodiments, the acquiring of the current state information of the user based on the gyroscope parameter and the current time can include: inputting the gyroscope parameter and the current time into a first state acquisition model to obtain a general state result output by the first state acquisition model, wherein the first state acquisition model is obtained based on device gyroscope parameters of at least two sample users in at least two different sample time periods and corresponding state labels; and inputting the general state result and the current time into a second state acquisition model to correct the general state result according to the current time by the second state acquisition model, acquire and output the current state information, wherein the second state acquisition model is obtained based on general state results of the user in different time periods and corresponding state labels fed back by the user.

[0057] In this way, the general state result is first acquired by the first state acquisition model, and then the general state result is corrected according to the current time based on the second state acquisition model corresponding to the user, so that the personalized behavior habits of the user implied in the second state acquisition model can be used to output more accurate and more personalized state results for specific users, and the effect of subsequent video recommendation is further improved.

[0058] In some embodiments, the first state acquisition model described above can be constructed based on a neural network such as a multi-layer perception network, a Long Short-Term Memory (LSTM) network, or a Transformer network. The sample data used to train the state prediction model can include device gyroscope parameters of a plurality of sample users when they are watching a video at different time periods, corresponding time points (or time periods), and state labels. By inputting a set of gyroscope parameters of a sample user at a certain time period into the state prediction model, a state prediction result output by the model is obtained, and a loss is calculated based on the state prediction result and the state label to train the model.

[0059] In some embodiments, the second state acquisition model described above can be constructed based on a neural network such as a multi-layer perception network, a Long Short-Term Memory (LSTM) network, or a Transformer network. The second state acquisition model can be a model corresponding to the target device.

[0060] In some embodiments, the second state acquisition model can be trained based on the general state results of a plurality of different time periods predicted by the first state acquisition model, the corresponding time points (or time periods), and the real state labels fed back by the user, so that the trained second state acquisition model can learn the user's individualized behavior habits (implicitly embodied in the second state acquisition model weight parameters), and then the general state result can be corrected according to the current time point based on the learned behavior habits of the user.

[0061] For example, most users are usually in a meal state at 12:00-13:00, so the first state acquisition model trained by the relevant data of a large number of sample users is more inclined to predict the state of this time period as a meal state. If the meal time of individual users differs due to individual habit differences, the general state result of the current time point output by the first state acquisition model can be corrected according to the current time point based on the user's behavior habits implicitly embodied in the second state acquisition model corresponding to the target device of the user to obtain a more accurate prediction result of the current state information. For example, the individual user's habit is to be in a meal state at 11:00-12:00 and in a lunch break leisure state at 12:00-13:00. The first state acquisition model may, based on the device gyroscope parameters of the user and the current time point (e.g., 12:20), predict the general state result of the user as a meal state. Based on the trained second state acquisition model corresponding to the user, the general state result can be corrected to a lunch break leisure state that is more in line with the user's individualized habits according to the current time point.

[0062] In some embodiments, since the real state information fed back by the user can be less, the second state acquisition model can be fine-tuned based on the real state information fed back by the user after the first state acquisition model and the second state acquisition model are jointly trained first, so as to further improve the accuracy and training efficiency of the second state acquisition model.

[0063] In some embodiments, inputting the gyroscope parameters and the current time into the first state acquisition model, and obtaining the general state result output by the first state acquisition model can include: inputting the gyroscope parameters, the current time and the historical state before the current time into the first state acquisition model, so as to obtain and output the general state result by the first state acquisition model according to the historical state.

[0064] Wherein, the first state acquisition model can be trained based on a method similar to the above, which will not be repeated here.

[0065] Since the state of the user has a certain persistence, one or more historical states predicted in advance can be introduced into the state prediction process, so as to further improve the accuracy of state prediction and further improve the effect of subsequent video recommendation.

[0066] In some embodiments, as shown in Figure 3 Based on the current state information and the portrait information of the user, obtaining the target recommended video can include: step S301, recalling candidate recommended videos in the candidate video library based on the portrait information; step S302, obtaining the feedback operation information to be performed by the user on each candidate recommended video based on the current state; and step S303, determining the target recommended video in the candidate recommended video based on the feedback operation information.

[0067] Therefore, by using the current state information of the user, the feedback operation that the user is likely to perform on each candidate video is further predicted, and the target recommended video is determined based on the prediction result of the feedback operation, so that the user can be provided with a video that is more in line with the user's preferences and expectations, thereby improving the user experience.

[0068] In some embodiments, recalling multiple candidate recommended videos in the candidate video library based on the portrait information can be implemented based on the video recommendation model described above.

[0069] In some embodiments, recalling multiple candidate recommended videos in the candidate video library based on the portrait information can further include inputting the portrait information of the user, the videos watched by the user within a period of time before the current time, and the feedback operation of the user on the watched videos and other information into the video recommendation model, so that the video recommendation model comprehensively analyzes the above information and recalls multiple candidate recommended videos in the video library.

[0070] In some embodiments, for each of the plurality of candidate recommended videos, the feedback operation that the user is likely to perform on the candidate recommended video can be predicted based on the current state information by a pre-trained feedback operation prediction model. The predicted current state information of the user and video information (e.g., which can include video description information, video pictures, etc.) of each candidate recommended video can be input into the prediction model, and by comprehensively analyzing the above information, the feedback operation that the user is most likely to perform on the video can be predicted.

[0071] In some embodiments, the above feedback operation prediction model can be constructed based on a neural network such as a long short-term memory network or a Transformer network, and each sample data used to train the model can include current state information of a sample user, video information of a sample video, and a corresponding real feedback operation label.

[0072] In some embodiments, when recalling a plurality of candidate recommended videos, the video recommendation model can score the recommendation degree of each candidate recommended video. Based on the feedback operation prediction result of each candidate recommended video, at least one target recommended video can be determined from the plurality of candidate recommended videos by weighting the scores corresponding to the candidate recommended videos according to the weights corresponding to different feedback operation prediction results, thereby obtaining the weighted scores corresponding to each candidate recommended video, and the videos with the highest weighted scores are determined as the target recommended videos.

[0073] In some embodiments, based on the feedback operation information, the target recommended video can be determined from the candidate recommended videos by determining, as the target recommended video, a candidate recommended video in which the feedback operation information indicates a positive feedback operation, wherein the positive feedback operation includes at least one of liking, commenting, sharing, and watching the entire content of the video. In this way, by filtering videos on which the user is inclined to give positive feedback operations, the user experience can be further improved.

[0074] In some embodiments, as shown in FIG. 4, Figure 4 A video recommendation apparatus 400 is provided, which includes a first obtaining unit 410 configured to obtain gyroscope parameters of a target device operated by a user within a preset time period before a current time; a second obtaining unit 420 configured to obtain current state information of the user based on the gyroscope parameters, the current state information including posture information of the user when operating the target device at the current time; a third obtaining unit 430 configured to obtain a target recommended video based on the current state information and portrait information of the user; and a recommendation unit 440 configured to recommend the target recommended video to the target device.

[0075] The operations performed by the units 410-440 in the video recommendation apparatus 400 and the effects that can be achieved are similar to steps S201-S204 in the video recommendation method described above, and thus will not be described here.

[0076] In some embodiments, the second obtaining unit can be further configured to obtain the current state information of the user based on the gyroscope parameter and the current time.

[0077] In some embodiments, the second obtaining unit can include a first obtaining subunit configured to input the gyroscope parameter and the current time into a first state obtaining model to obtain a general state result output by the first state obtaining model, wherein the first state obtaining model is obtained based on device gyroscope parameters of at least two sample users in at least two different sample time periods and corresponding state labels; and a second obtaining subunit configured to input the general state result and the current time into a second state obtaining model to correct the general state result according to the current time by the second state obtaining model, and obtain and output the current state information, wherein the second state obtaining model is obtained based on general state results of the user in different time periods and corresponding state labels of user feedback.

[0078] In some embodiments, the first obtaining subunit can be further configured to input the gyroscope parameter, the current time, and a historical state before the current time into the first state obtaining model to obtain and output the general state result according to the historical state by the first state obtaining model.

[0079] In some embodiments, the third obtaining unit can include a recall subunit configured to recall candidate recommendation videos from a candidate video library based on the portrait information; a third obtaining subunit configured to obtain feedback operation information of the user to be performed on each candidate recommendation video based on the current state; and a first determining subunit configured to determine a target recommendation video from the candidate recommendation videos based on the feedback operation information.

[0080] In some embodiments, the first determining subunit can be further configured to determine, as the target recommendation video, a candidate recommendation video in the candidate recommendation videos for which the feedback operation information indicates a positive feedback operation, wherein the positive feedback operation includes at least one of liking, commenting, sharing, and watching the entire content of a video.

[0081] In the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solutions comply with relevant laws and regulations and do not violate public order and good customs.

[0082] According to embodiments of the present disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0083] Reference Figure 5 A block diagram of an electronic device 500, which can be an example of a hardware device that can be applied to aspects of the disclosure, will now be described, which is an example of a hardware device that can be applied to aspects of the disclosure. The electronic device is intended to represent various forms of digital electronic computer devices such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0084] As Figure 5 shown, the electronic device 500 includes a computing unit 501 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 502 or a computer program loaded into a random access memory (RAM) 503 from a storage unit 508. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0085] A plurality of components in the electronic device 500 are connected to the I / O interface 505, including an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 can be any type of device that can input information to the electronic device 500, can receive inputted digital or character information, and generate key signal inputs related to user settings and / or function controls of the electronic device, and can include, but is not limited to, a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and / or a remote controller. The output unit 507 can be any type of device that can present information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0086] The computing unit 501 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, and the like. The computing unit 501 performs various methods and processes described above, such as the video recommendation method described above. For example, in some embodiments, the video recommendation method described above can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded onto the RAM 503 and executed by the computing unit 501, one or more steps of the video recommendation method described above can be performed. Alternatively, in other embodiments, the computing unit 501 can be configured to perform the video recommendation method described above by any other appropriate means, such as by means of firmware.

[0087] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0088] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0089] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can 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 the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, 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.

[0090] 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 the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; 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 can be received in any form, including acoustic, speech, or tactile input.

[0091] 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 a local area network (LAN), a wide area network (WAN), and the Internet.

[0092] The computer system can 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 can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0093] It should be understood that the various forms of flow illustrated above can be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technology disclosed in the present disclosure can be achieved, which is not limited herein.

[0094] While embodiments or examples of the present disclosure have been described with reference to the drawings, it should be understood that the above-described methods, systems, and devices are merely exemplary embodiments or examples, and the scope of the present disclosure is not limited by these embodiments or examples, but is only limited by the claims and their equivalents. Various elements in the embodiments or examples can be omitted or replaced by equivalent elements thereof. In addition, each step can be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples can be combined in various ways. It is important that many of the elements described herein can be replaced by equivalent elements that appear after the present disclosure as technology evolves.

Claims

1. A video recommendation method, comprising: obtaining a gyroscope parameter within a preset time period before a current time of a target device being operated by a user; obtaining current state information of the user based on the gyroscope parameter, the current state information comprising posture information of the user when operating the target device at the current time, wherein the obtaining the current state information of the user based on the gyroscope parameter comprises: obtaining the current state information of the user based on the gyroscope parameter and the current time, comprising: inputting the gyroscope parameter and the current time into a first state obtaining model to obtain a general state result output by the first state obtaining model, wherein the first state obtaining model is obtained based on device gyroscope parameters of at least two sample users in at least two different sample time periods and corresponding state labels; and inputting the general state result and the current time into a second state obtaining model to correct the general state result according to the current time by the second state obtaining model, to obtain and output the current state information, wherein the second state obtaining model is obtained based on general state results of the user in different time periods and corresponding state labels fed back by the user; obtaining a target recommendation video based on the current state information and profile information of the user; and recommending the target recommendation video to the target device.

2. The method of claim 1, wherein, The inputting the gyroscope parameter and the current time into a first state obtaining model to obtain a general state result output by the first state obtaining model comprises: inputting the gyroscope parameter, the current time and a historical state before the current time into the first state obtaining model to obtain and output the general state result according to the historical state by the first state obtaining model.

3. The method of claim 1 or 2, wherein, The obtaining a target recommendation video based on the current state information and profile information of the user comprises: recalling candidate recommendation videos from a candidate video library based on the profile information; obtaining feedback operation information of the user to be performed on each of the candidate recommendation videos based on the current state; and determining the target recommendation video from the candidate recommendation videos based on the feedback operation information.

4. The method of claim 3, wherein, The determining the target recommendation video from the candidate recommendation videos based on the feedback operation information comprises: determining a candidate recommendation video in which the feedback operation information indicates a positive feedback operation as the target recommendation video, wherein the positive feedback operation comprises at least one of liking, commenting, sharing and watching the entire content of a video. 5.A video recommendation apparatus, comprising: a first obtaining unit configured to obtain a gyroscope parameter within a preset time period before a current time of a target device being operated by a user; a second obtaining unit, configured to obtain current state information of the user based on the gyroscope parameter, the current state information comprising posture information of the user when operating the target device at a current time, wherein the second obtaining unit is further configured to obtain the current state information of the user based on the gyroscope parameter and the current time, and wherein the second obtaining unit comprises: a first obtaining sub-unit, configured to input the gyroscope parameter and the current time into a first state obtaining model to obtain a general state result output by the first state obtaining model, wherein the first state obtaining model is obtained based on device gyroscope parameters of at least two sample users at at least two different sample time periods and corresponding state labels; a second obtaining sub-unit, configured to input the general state result and the current time into a second state obtaining model to obtain and output the current state information by correcting the general state result according to the current time by the second state obtaining model, wherein the second state obtaining model is obtained based on general state results of the user at different time periods and corresponding state labels fed back by the user; a third obtaining unit, configured to obtain a target recommended video based on the current state information and portrait information of the user; and a recommending unit, configured to recommend the target recommended video to the target device.

6. The apparatus of claim 5, wherein, The first obtaining sub-unit is further configured to: input the gyroscope parameter, the current time and a historical state before the current time into the first state obtaining model to obtain and output the general state result according to the historical state by the first state obtaining model.

7. The apparatus of claim 5 or 6, wherein, The third obtaining unit comprises: a recalling sub-unit, configured to recall candidate recommended videos from a candidate video library based on the portrait information; a third obtaining sub-unit, configured to obtain feedback operation information of the user to be performed on each of the candidate recommended videos based on the current state; and a first determining sub-unit, configured to determine the target recommended video from the candidate recommended videos based on the feedback operation information.

8. The apparatus of claim 7, wherein, The first determining sub-unit is further configured to: determine, as the target recommended video, a candidate recommended video in which the feedback operation information indicates a positive feedback operation, wherein the positive feedback operation comprises at least one of liking, commenting, sharing and watching the entire content of a video.

9. An electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.

10. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable a computer to perform the method of any one of claims 1-4.

11. A computer program product comprising a computer program, wherein, The computer program, when executed by a processor, implements the method of any one of claims 1-4. The computer program, when executed by a processor, implements the method of any one of claims 1-4.

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