Video recommendation method and device, medium and equipment

Abstract

The invention provides a video recommendation method and device, a medium and equipment, and relates to the technical field of artificial intelligence, in particular to the technical field of deep learning and video recommendation. According to the implementation scheme, the method comprises the following steps: acquiring gyroscope parameters of target equipment operated by a user within a preset time period before the current moment; acquiring current state information of the user based on the gyroscope parameters; obtaining a target recommended video based on the current state information and the 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 field of artificial intelligence technology, in particular to the field of deep learning and video recommendation technology, and specifically to a video recommendation method, device, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] Artificial intelligence is a discipline that studies how to use computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0003] With the continuous development of online video technology, accurately recommending videos that users need has become an important means to increase the number of clicks on online videos. When users see that the recommended videos are exactly what they need, they will click on them without thinking, which is convenient for users and conducive to the development of online video platforms.

[0004] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

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

[0006] According to one aspect of the present disclosure, a video recommendation method is provided, including: obtaining gyroscope parameters of a target device that is being operated by a user within a preset period of time before a current moment; based on the gyroscope parameters, 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 moment; 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, including: a first acquisition unit, configured to acquire gyroscope parameters within a preset time period before a current moment of a target device being operated by a user; a second acquisition unit, configured to acquire 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 moment; a third acquisition unit, configured to acquire 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 to 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 so that the at least one processor can execute 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 execute the above-mentioned video recommendation method.

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

[0011] According to one or more embodiments of the present disclosure, it is possible to recommend videos that are more suitable for the current status to users, thereby improving user experience.

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

[0013] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0014] Figure 1 A schematic diagram showing an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

[0015] Figure 2 A flowchart of a video recommendation method according to an embodiment of the present disclosure is shown;

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

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

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

[0019] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0020] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

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

[0022] With the rapid development of short video platforms, accurate video recommendations have 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.) to make video recommendations. In practical applications, the accuracy of recommendations still needs to be improved.

[0023] The present disclosure provides a video recommendation method, which obtains the gyroscope parameters of a target device that is being operated by a user, predicts the state of the user currently using the target device based on the gyroscope parameters, and then recommends videos based on the current state and the user's portrait information, thereby being able to more accurately recommend videos that are more in line with the current state to the user, thereby improving the user experience.

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

[0025] Figure 1 FIG. 1 is a schematic diagram of an exemplary system 100 in which various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. 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 may be configured to execute one or more applications.

[0026] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable the above-mentioned video recommendation method to be performed.

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

[0028] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

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

[0030] Client devices 101, 102, 103, 104, 105 and / or 106 may 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 terminal devices, service robots, game systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may 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 include various mobile operating systems, such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular phones, smart phones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Game systems may include various handheld game devices, Internet-enabled game devices, etc. Client devices are capable of executing various different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and may use various communication protocols.

[0031] The network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0032] Server 120 may include one or more general purpose computers, dedicated server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

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

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

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

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

[0037] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

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

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

[0040] Therefore, by obtaining the gyroscope parameters of the target device that the user is operating, the user's current state of using the target device is predicted based on the gyroscope parameters, and then video recommendations are made based on the current state and the user's portrait information. This allows for more accurate recommendations of videos that are more in line with the current state, thereby improving the user experience.

[0041] In some embodiments, the target device may be a mobile phone, a tablet computer, or any other handheld terminal device, which is not limited here.

[0042] In some embodiments, the gyroscope parameters of the target device may be obtained at preset time intervals, and when predicting the user's status information at the current moment, multiple groups of gyroscope parameters within a preset period before the current moment may be obtained.

[0043] In some embodiments, gyroscope parameters may include, but are not limited to, angular velocity, acceleration, and device tilt angle, which are not limited herein.

[0044] In some embodiments, the user's current state information may represent the potential behavior characteristics of the user currently watching a video, and may include the posture information maintained by the user when operating the target device at the current moment, for example, whether the user is holding the target device at the current moment, whether the user is sitting, lying or walking, whether the user is eating, etc. It is understandable that relevant technicians can set a variety of current state information according to actual conditions, and there is no limitation here.

[0045] In some embodiments, the prediction of the user's current state information based on the gyroscope parameters can be implemented based on a pre-trained state prediction model. A set of gyroscope parameters within a preset period before the current moment can be input into the state prediction model to predict the user's current state information using the model.

[0046] In some embodiments, the state prediction model can be constructed based on a neural network such as a multi-layer perceptron network, a long short-term memory network (LSTM) or a Transformer network, and the sample data used to train the state prediction model can include device gyroscope parameters of multiple sample users when they are watching videos in multiple different time periods and corresponding state labels. A set of gyroscope parameters of a sample user in a certain time period is input into the state prediction model to obtain the state prediction result output by the model, and the loss is calculated based on the state prediction result and the state label to train the model.

[0047] In some embodiments, video recommendation is performed based on current status information and user portrait information to obtain at least one target recommended video. The current status information and user portrait information obtained above are input into a video recommendation model. The video recommendation model performs a comprehensive analysis on the above information and recalls at least one target recommended video in the recommended video library based on the analysis results.

[0048] In some embodiments, the user's portrait information may include, but is not limited to, various types of information such as the user's gender, age, interests, and hobbies.

[0049] It should be noted that users are aware of and authorized the acquisition and use of the above-mentioned user portrait information and other user information.

[0050] In some embodiments, the video recommendation model can be obtained by fine-tuning and training based on a large model. In some embodiments, the video recommendation model can also be constructed and trained 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 may include the status information of sample users in the above-mentioned multiple different time periods, the videos watched, and the favorite labels for the watched videos, wherein the favorite labels for the watched videos can be determined based on the feedback operations performed by the users on the video. For example, when the user performs negative feedback operations such as swiping, clicking "not interested" or fast-forwarding" on the video, the favorite label of the video is marked as a negative label. When the user performs positive feedback operations such as liking, commenting, sharing, watching all the contents of the video, etc., the favorite label of the video is marked as a positive label.

[0052] After obtaining the target recommended video, the target recommended video can be pushed to the user's target device. Therefore, by obtaining the gyroscope parameters of the target device that the user is operating, the user's current state of using the target device is predicted based on the gyroscope parameters, and then the video is recommended based on the current state and the user's portrait information, so that the user can be more accurately recommended videos that are more suitable for the current state, thereby improving the user experience.

[0053] In some embodiments, the obtaining of the user's current status information based on the gyroscope parameters may include: obtaining the user's current status information based on the gyroscope parameters and the current time.

[0054] Since the user's state at different times may have certain regularities (for example, noon is usually lunch time, and the user's state is more likely to be in the dining state), the accuracy of the state prediction can be further improved by combining the current time and gyroscope parameters to predict the current state information.

[0055] In some embodiments, the prediction of the user's current state information based on the gyroscope parameters and the current moment can be implemented based on a pre-trained state prediction model. A set of gyroscope parameters within a preset period before the current moment and the current moment (or a preset period before the current moment) can be input into the state prediction model to predict the user's current state information obtained by the model. It is understandable 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 repeated here.

[0056] In some embodiments, based on gyroscope parameters and the current moment, obtaining the user's current state information may include: inputting the gyroscope parameters and the current moment into a first state acquisition model, obtaining a general state result output by the first state acquisition model, wherein the first state acquisition model is trained based on device gyroscope parameters and corresponding state labels of at least two sample users in at least two different sample time periods; and inputting the general state result and the current moment into a second state acquisition model to correct the general state result according to the current moment through the second state acquisition model, to obtain and output the current state information, wherein the second state acquisition model is trained based on the general state results of the user in different time periods and the corresponding state labels of user feedback.

[0057] Therefore, firstly, the general state result is obtained through the first state acquisition model, and then based on the second state acquisition model corresponding to the user, the general state result is corrected according to the current moment, so that it can be achieved based on the personalized behavior habits of the user implicit in the second state acquisition model, and output a more accurate and personalized state result for a specific user, thereby further improving the effect of subsequent video recommendations.

[0058] In some embodiments, the first state acquisition model can be constructed based on a neural network such as a multi-layer perception network, a long short-term memory network (LSTM) or a Transformer network, and the sample data used to train the state prediction model can include device gyroscope parameters of multiple sample users when they are watching videos in multiple different time periods, corresponding moments (or time periods) and state labels. A set of gyroscope parameters of a certain time period of the sample user is input into the state prediction model to obtain the state prediction result output by the model, and the 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 can be constructed based on a neural network such as a multi-layer perceptron network, a long short-term memory 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 multiple different time periods predicted by the first state acquisition model, the corresponding moments (or time periods) and the actual state labels fed back by the user, so that the trained second state acquisition model can learn the user's personalized behavior habits (implicitly reflected in the weight parameters of the second state acquisition model), and then based on the learned behavior habits of the user, the general state results can be corrected according to the current moment.

[0061] Exemplarily, most users are usually in a dining state between 12:00 and 13:00, and the first state acquisition model obtained by training with relevant data of a large number of sample users is more inclined to predict the state of this period as a dining state; if due to individual habit differences, the dining time of individual users may be different, then the general state result of the current moment output by the first state acquisition model can be corrected based on the user behavior habits implied in the second state acquisition model according to the current moment through the second state acquisition model corresponding to the target device of the user, so as to obtain a more accurate prediction result of the current state information. For example, the habit of an individual user is to be in a dining state between 11:00 and 12:00, and to be in a lunch break and leisure state between 12:00 and 13:00. The first state acquisition model may predict the user's general state result as a dining state based on the gyroscope parameters of the user's device and the current moment (such as 12:20), and the second state acquisition model corresponding to the user after training can correct the above general state result to a lunch break and leisure state that is more in line with the user's personalized habits according to the current moment.

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

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

[0064] Among them, the first state acquisition model can be trained based on a method similar to that described above, which will not be elaborated here.

[0065] Since the user's state has a certain continuity, one or more historical states predicted in the previous order can be introduced into the state prediction process, thereby further improving the accuracy of state prediction and the effect of subsequent video recommendations.

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

[0067] Therefore, through the user's current state information, the feedback operation that the user may perform is further predicted for each candidate video, and the target recommended video is determined based on the predicted result of the feedback operation, which can provide the user with videos that are 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 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 portrait information can further include inputting the user's portrait information, videos the user has watched within a period of time before the current moment, and the user's feedback operations on the watched videos into the video recommendation model, so that the video recommendation model conducts a comprehensive analysis of the above information and recalls multiple candidate recommended videos in the video library.

[0070] In some embodiments, for each candidate recommended video among multiple candidate recommended videos, predicting the feedback operation that the user may perform on the candidate recommended video based on the current state information can be implemented by a pre-trained feedback operation prediction model. The predicted current state information of the user and the video information of each candidate recommended video (for example, it may include video description information, video screen, etc.) can be input into the prediction model, and by comprehensively analyzing the above information, the feedback operation that the user is likely to perform on the video can be predicted.

[0071] In some embodiments, the 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 may include the current state information of the sample user, the video information of the sample video, and the corresponding real feedback operation label.

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

[0073] In some embodiments, based on the feedback operation information, determining the target recommended video from the candidate recommended videos may include: determining the candidate recommended video whose feedback operation information indicates a positive feedback operation from the candidate recommended videos as the target recommended video, wherein the positive feedback operation includes at least one of liking, commenting, sharing, and watching the entire content of the video. Thus, by screening videos that users tend to give positive feedback operations for recommendation, the user experience can be further improved.

[0074] In some embodiments, Figure 4 As shown, a video recommendation device 400 is provided, including: a first acquisition unit 410, configured to acquire gyroscope parameters of a target device that is being operated by a user within a preset time period before a current moment; a second acquisition unit 420, configured to acquire 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 moment; a third acquisition unit 430, configured to acquire a target recommended video based on the current state information and the user's portrait information; and a recommendation unit 440, configured to recommend the target recommended video to the target device.

[0075] The operations performed and the effects achieved by the units 410 to 440 in the video recommendation device 400 are similar to the steps S201 to S204 in the above-mentioned video recommendation method, and are not described in detail here.

[0076] In some embodiments, the second acquisition unit may be further configured to: acquire the current state information of the user based on the gyroscope parameters and the current time.

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

[0078] In some embodiments, the first acquisition subunit may be further configured to input the gyroscope parameters, the current moment and the historical state before the current moment into the first state acquisition model, so as to acquire and output a general state result according to the historical state through the first state acquisition model.

[0079] In some embodiments, the third acquisition unit may include: a recall sub-unit, configured to recall candidate recommended videos in the candidate video library based on portrait information; a third acquisition sub-unit, configured to obtain feedback operation information to be performed by the user on each candidate recommended video based on the current state; and a first determination sub-unit, configured to determine the target recommended video among the candidate recommended videos based on the feedback operation information.

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

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

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

[0083] refer to Figure 5 , a block diagram of an electronic device 500 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0084] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. 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 via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0085] Multiple 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. The input unit 506 can receive input digital or character information and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, 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 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 may be a variety of general and / or special 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 dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as the above-mentioned video recommendation method. For example, in some embodiments, the above-mentioned video recommendation method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the above-mentioned video recommendation method may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the above-mentioned video recommendation method in any other appropriate manner (e.g., by means of firmware).

[0087] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0088] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0089] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0090] To provide interaction with a user, the systems and techniques described herein 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 input, voice input, or tactile input).

[0091] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may 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] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may 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 processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

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

Claims

1. A video recommendation method, comprising: Obtaining gyroscope parameters of a target device being operated by a user within a preset period of time before the current moment; Based on the gyroscope parameters, 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 moment; Based on the current state information and the user's portrait information, obtaining a target recommended video; as well as The target recommended video is recommended to the target device.

2. The method according to claim 1, wherein: The acquiring the current state information of the user based on the gyroscope parameters comprises: Based on the gyroscope parameters and the current time, the current state information of the user is obtained.

3. The method according to claim 2, wherein: The acquiring the current state information of the user based on the gyroscope parameter and the current time includes: Inputting the gyroscope parameters and the current moment 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 after training based on device gyroscope parameters and corresponding state labels of at least two sample users in at least two different sample periods; and The general status result and the current moment are input into a second status acquisition model, so as to correct the general status result according to the current moment through the second status acquisition model, acquire and output the current status information, wherein the second status acquisition model is obtained after training based on the general status results of the user in different time periods and the corresponding status labels fed back by the user.

4. The method according to claim 3, wherein: The step of inputting the gyroscope parameters and the current moment into a first state acquisition model to obtain a general state result output by the first state acquisition model comprises: The gyroscope parameters, the current moment, and the historical state before the current moment are input into the first state acquisition model, so as to acquire and output the general state result according to the historical state through the first state acquisition model.

5. The method according to any one of claims 1 to 4, wherein: The acquiring of a target recommended video based on the current state information and the user's portrait information includes: Based on the portrait information, recall candidate recommended videos from the candidate video library; Based on the current state, obtaining feedback operation information to be performed by the user on each of the candidate recommended videos; and Based on the feedback operation information, the target recommended video is determined from among the candidate recommended videos.

6. The method according to claim 5, wherein: The determining the target recommended video from the candidate recommended videos based on the feedback operation information comprises: Among the candidate recommended videos, the candidate recommended video whose feedback operation information indicates a positive feedback operation is determined as the target recommended video, wherein the positive feedback operation includes at least one of liking, commenting, sharing, and watching the entire content of the video.

7. A video recommendation device, comprising: A first acquisition unit is configured to acquire gyroscope parameters within a preset period before a current moment of a target device being operated by a user; A second acquisition unit is configured to acquire the current state information of the user based on the gyroscope parameter, wherein the current state information includes the posture information of the user when operating the target device at the current moment; A third acquisition unit is configured to acquire a target recommended video based on the current state information and the portrait information of the user; as well as The recommendation unit is configured to recommend the target recommended video to the target device.

8. The device according to claim 7, wherein: The second acquiring unit is further configured to: Based on the gyroscope parameters and the current time, the current state information of the user is obtained.

9. The device according to claim 8, wherein: The second acquiring unit includes: a first acquisition subunit, configured to input the gyroscope parameters and the current moment into a first state acquisition model, and acquire a general state result output by the first state acquisition model, wherein the first state acquisition model is obtained after training based on device gyroscope parameters and corresponding state labels of at least two sample users in at least two different sample periods; and The second acquisition subunit is configured to input the general status result and the current moment into a second status acquisition model, so as to correct the general status result according to the current moment through the second status acquisition model, and acquire and output the current status information, wherein the second status acquisition model is obtained after training based on the general status results of the user in different time periods and the corresponding status labels fed back by the user.

10. The device according to claim 9, wherein: The first acquisition subunit is further configured to: The gyroscope parameters, the current moment, and the historical state before the current moment are input into the first state acquisition model, so as to acquire and output the general state result according to the historical state through the first state acquisition model.

11. The device according to any one of claims 7 to 10, wherein: The third acquisition unit includes: A recall subunit, configured to recall candidate recommended videos in a candidate video library based on the portrait information; A third acquisition subunit is configured to acquire feedback operation information to be performed by the user on each of the candidate recommended videos based on the current state; and The first determination subunit is configured to determine the target recommended video from among the candidate recommended videos based on the feedback operation information.

12. The device according to claim 11, wherein The first determining subunit is further configured to: Among the candidate recommended videos, the candidate recommended video whose feedback operation information indicates a positive feedback operation is determined as the target recommended video, wherein the positive feedback operation includes at least one of liking, commenting, sharing, and watching the entire content of the video.

13. An electronic device, comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed 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 method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to make a computer execute the method according to any one of claims 1-6.

15. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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