CN112685599B - Video recommendation method and device - Google Patents
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Abstract
The method determines the video type to be played by a client, if the video type belongs to a target type, the running state of the client is acquired, and at least one video of the recommended type is acquired according to the running state of the client, wherein the video of the recommended type belongs to the target type and is matched with the running state of the client. For example, the running states of the client include a foreground running state and a background running state, and if the client is in the foreground running state, a recommended type video matched with the foreground running state is acquired; and if the client is in the background running state, acquiring the recommended type video matched with the background running state. According to the scheme, when the video is recommended to the user, the recommended video matched with the running state of the client can be recommended, so that the matching degree between the recommended video and the running state of the client is improved, and finally the accuracy of video recommendation is improved.
Description
Technical Field
The disclosure relates to the field of computer technology, and in particular, to a video recommendation method and device.
Background
With the popularization of mobile terminals and the acceleration of networks, short and quick mass-flow propagation contents are gradually favored by platforms and vast users. Video content for video platforms includes a wide variety of content.
The current video playing method usually plays the next video in the video stream automatically after playing one video in the form of video stream, however, the video recommendation accuracy in the related art is lower.
Disclosure of Invention
The disclosure provides a video recommendation method and device, which at least solve the problem of low accuracy of video recommendation results in the related art. The technical scheme of the present disclosure is as follows:
according to a first aspect of an embodiment of the present disclosure, there is provided a video recommendation method, including:
determining the type of the video to be played by the client;
when the video type is determined to belong to a target type, acquiring the running state of the client;
and acquiring at least one recommended type video according to the running state of the client, wherein the at least one recommended type video belongs to the target type and is matched with the running state of the client.
In a possible implementation manner of the first aspect, the obtaining at least one recommended type video according to an operation state of the client includes:
when the client is determined to be in a foreground running state, at least one first recommendation type video is obtained, wherein the at least one first recommendation type video accords with a foreground recommendation rule matched with the foreground running state, and the foreground recommendation rule at least comprises: the video picture contains a human face.
In another possible implementation manner of the first aspect, the foreground recommendation rule at least further includes: the audio quality of the video is not lower than a first preset audio quality threshold.
In a further possible implementation manner of the first aspect, the foreground recommendation rule at least further includes: the user feedback quality score of the video is higher than a preset user feedback quality threshold, and the user feedback quality score is obtained according to user behaviors generated when the user watches the video.
In still another possible implementation manner of the first aspect, the acquiring at least one recommended type video according to the running state of the client further includes:
when the client is determined to be in a background running state, at least one second recommendation type video is obtained, wherein the at least one second recommendation type video accords with a background recommendation rule matched with the background running state, and the background recommendation rule at least comprises: the audio quality of the video is greater than a second preset audio quality threshold, which is greater than the first preset audio quality threshold.
In another possible implementation manner of the first aspect, the determining a video type to be played by the client includes:
And in response to detecting a selection operation of selecting a video type to be played on the client by a user, determining the video type corresponding to the selection operation as the video type to be played on the client.
In yet another possible implementation manner of the first aspect, the determining a video type to be played by the client includes:
and when the video types played by the client in the preset time period before the current moment belong to the same video type, determining the same video type as the video type to be played by the client.
According to a second aspect of embodiments of the present disclosure, there is provided a video recommendation apparatus, the apparatus comprising:
the video type determining module is configured to determine the type of the video to be played by the client;
the running state acquisition module is configured to acquire the running state of the client when the video type is determined to belong to a target type;
the recommended video acquisition module is configured to acquire at least one recommended type video according to the running state of the client, wherein the at least one recommended type video belongs to the target type and is matched with the running state of the client.
In a possible implementation manner of the second aspect, the recommended video acquisition module includes:
The first obtaining sub-module is configured to obtain at least one first recommendation type video when the client is determined to be in a foreground running state, wherein the at least one first recommendation type video accords with a foreground recommendation rule matched with the foreground running state, and the foreground recommendation rule at least comprises: the video picture contains a human face.
In another possible implementation manner of the second aspect, the foreground recommendation rule at least further includes: the audio quality of the video is not lower than a first preset audio quality threshold.
In a further possible implementation manner of the second aspect, the foreground recommendation rule at least further includes: the user feedback quality score of the video is higher than a preset user feedback quality threshold, and the user feedback quality score is obtained according to user behaviors generated when the user watches the video.
In a further possible implementation manner of the second aspect, the recommended video acquisition module further includes:
the second obtaining sub-module is configured to obtain at least one second recommendation type video when the client is determined to be in a background running state, wherein the at least one second recommendation type video accords with a background recommendation rule matched with the background running state, and the background recommendation rule at least comprises: the audio quality of the video is greater than a second preset audio quality threshold, which is greater than the first preset audio quality threshold.
In another possible implementation manner of the second aspect, the video type determining module includes:
the first determining submodule is configured to respond to detection of selection operation of selecting a video type to be played on the client by a user, and determine the video type corresponding to the selection operation as the video type to be played by the client.
In a further possible implementation manner of the second aspect, the video type determining module includes:
and the second determining submodule is configured to determine the same video type as the video type to be played by the client when the video type played by the client in a preset time period before the current moment belongs to the same video type.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the video recommendation method according to any one of the possible implementations of the first aspect.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer readable storage medium having instructions stored thereon, which when executed by a processor of an electronic device, enable the electronic device to perform the video recommendation method according to any one of the possible implementations of the first aspect.
According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product having instructions stored therein, which when executed by a processor in an electronic device, implement the video recommendation method according to any one of the possible implementations of the first aspect.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects: determining the video type to be played by the client, if the video type belongs to the target type, acquiring the running state of the client, and acquiring at least one recommended type video according to the running state of the client, wherein the recommended type video belongs to the target type and is matched with the running state of the client. For example, the running states of the client include a foreground running state and a background running state, and if the client is in the foreground running state, a recommended type video matched with the foreground running state is acquired; and if the client is in the background running state, acquiring the recommended type video matched with the background running state. According to the scheme, when the video is recommended to the user, the recommended video matched with the running state of the client can be recommended, so that the matching degree between the recommended video and the running state of the client is improved, and finally the accuracy of video recommendation is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure and do not constitute an undue limitation on the disclosure.
FIG. 1 is a flowchart illustrating a video recommendation method according to an exemplary embodiment;
FIG. 2 is a flowchart illustrating another video recommendation method, according to an example embodiment;
FIG. 3 is a flowchart illustrating yet another video recommendation method, according to an exemplary embodiment;
FIG. 4 is a block diagram of a video recommendation device, according to an example embodiment;
FIG. 5 is a block diagram of another video recommendation device, shown in accordance with an exemplary embodiment;
fig. 6 is a block diagram of an electronic device, according to an example embodiment.
Detailed Description
In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
Fig. 1 is a flowchart illustrating a video recommendation method for use in a client, as shown in fig. 1, according to an exemplary embodiment, the video recommendation method including the following steps.
In S110, a video type to be played by the client is determined.
When the client starts and plays the video, determining the video type of the video to be played by the client, wherein the video type at least comprises a music type and the like. The video type to be played is the video type of the video to be played.
In one application scenario, a user selects a video type to be watched on a display interface of a client, and determines that the video type selected by the user is a video type to be played by the client.
In such an application scenario, the process of determining the video type to be played by the client may include: and in response to detecting a selection operation of selecting a video type to be played on the client by a user, determining the video type corresponding to the selection operation as the video type to be played on the client.
In one possible implementation manner, a control matched with each video type is displayed on a display interface of the client, the selection operation of selecting and playing the video type by the user may be a touch operation of touching a certain video type control by the user, and the video type corresponding to the touched video type control is the video type selected by the user.
In another application scenario, the user does not select the video type to be watched, but analyzes the historical play data of the user to find that the video types watched by the user in the preset time period are all the same video type.
In such an application scenario, the process of determining the video type to be played by the client may include: and when the video types played by the client in the preset time period before the current moment belong to the same video type, determining the same video type as the video type to be played by the client.
In S120, when it is determined that the video type is the target type, the running state of the client is acquired.
The running states of the client comprise a foreground running state and a background running state. In one embodiment of the present disclosure, a client may obtain running state information of the client from an operating system of a mobile terminal in which the client is installed. For example, an APP may obtain the running state of the APP from the system of the mobile phone.
In one possible implementation manner, an operating system of the mobile terminal determines whether the client is in a foreground running state or a background running state by monitoring whether a screen is off or on; and if the screen is off, determining that the client is in a background running state. If the screen is bright, the running state of the client needs to be further monitored to determine whether the client is currently in foreground running or background running.
In S130, at least one recommendation type video is acquired according to an operation state of the client.
Wherein the at least one recommended type video belongs to a target type and is matched with the running state of the client.
In one embodiment, the client sends its running state to the server to obtain a recommended type video from the server that matches the running state. For example, the client may actively send the request to the server after obtaining the running state of the client, or the server sends a request for obtaining the running state to the client, and the client returns the running state to the server after receiving the request.
In an application scenario, the style and quality of the video uploaded to the server by the user of the video APP are quite different, for example, for music video, there are face-exposed singing, face-unexposed singing, homemade MV (Music Video) with exquisite making, and simple and primitive playing. These differences in style can result in the viewer's look and feel being different, with some being better suited to viewing in the foreground operating state and some possibly being slightly worse, more suited to listening only to audio.
Therefore, the embodiment of the disclosure provides a pushing scheme capable of recommending different videos according to a foreground running state or a background running state of a client, and respectively setting different recommendation rules, such as a foreground recommendation rule and a background recommendation rule, according to different running states; if the client is in the foreground running state, recommending the video matched with the foreground recommendation rule; if the client is currently in the background running state, recommending the video matched with the background recommendation rule.
When the client is in a foreground running state, the user is generally considered to be watching the video content played by the APP at this time, and a video with better picture quality, for example, a video containing a face in a video picture, can be recommended to the user. When the client is in the background running state, a video suitable for background playing can be recommended to the user, for example, a music video is taken as an example, and the video suitable for background playing can be a video with lower picture quality but higher audio quality. The video with better picture quality can improve the watching rate of the foreground user, and the video with higher audio quality can improve the watching rate of the background user, so that the video recommending method improves the accuracy of video recommending.
According to the video recommendation method provided by the embodiment, the video type to be played by the client is determined, if the video type belongs to the target type, the running state of the client is obtained, and at least one recommendation type video is obtained according to the running state of the client, wherein the recommendation type video belongs to the target type and is matched with the running state of the client. For example, the running states of the client include a foreground running state and a background running state, and if the client is in the foreground running state, a recommended type video matched with the foreground running state is acquired; and if the client is in the background running state, acquiring the recommended type video matched with the background running state. According to the scheme, when the video is recommended to the user, the recommended video matched with the running state of the client can be recommended, so that the matching degree between the recommended video and the running state of the client is improved, and finally the accuracy of video recommendation is improved.
Fig. 2 is a flowchart illustrating another video recommendation method according to an exemplary embodiment, which includes the following steps, as shown in fig. 2.
In S210, a video type to be played by the client is determined.
In S220, when it is determined that the video type is the target type, the running state of the client is acquired.
In S230, when it is determined that the client is in the foreground running state, at least one first recommendation type video is acquired.
The first recommendation type video belongs to a target type and accords with a target type of a foreground recommendation rule.
In one embodiment, the foreground recommendation rules include at least: the video picture contains a human face.
In this case, when the video frame contains a face, it is determined that the video meets the foreground recommendation rule.
Taking music videos as an example for illustration, videos with exposed faces singing (namely, video pictures contain face images) or well-made MVs are more suitable for foreground playing, and the videos can improve the user watching rate, namely, the video is recommended to the user to meet the requirements of the user, in other words, the video recommendation accuracy is improved.
In another embodiment, the foreground recommendation rules include: the video picture comprises a human face, and the audio quality is not lower than a first preset audio quality threshold.
In this case, when the video frame contains a human face, the video is preliminarily determined to be suitable for foreground playing, the audio quality of the video is further evaluated, and if the audio quality is not lower than a first preset audio quality threshold, it is finally determined that the video meets a foreground recommendation rule.
Wherein the audio quality characterizes the quality of the audio content contained in the video. Taking a music video as an example for illustration, the audio content may be scored using a singing scoring model. A higher score indicates a higher audio quality and a lower score indicates a lower audio quality. In other embodiments, the audio quality may be represented by other parameters, for example, a rating of the audio quality is set, with a higher rating representing a higher audio quality and a lower rating representing a lower audio quality.
The first preset audio quality threshold may be set according to an actual application scenario, for example, the audio quality is represented by a score, and if the score is 100 minutes, the first preset audio quality threshold may be set to 60 minutes. Of course, in other embodiments of the present disclosure, other scores may be set, without limitation.
In yet another embodiment, the foreground recommendation rules further include: the user feedback quality score is above a preset user feedback quality threshold.
The user feedback quality score characterizes how well the video is liked by the viewer, e.g., a higher user feedback quality score indicates that the video is liked by the viewer, whereas a lower user feedback quality score indicates that the video is liked by the viewer. The user feedback quality score is derived from user behavior generated when the user views the video. The preset user feedback quality threshold can be set according to actual application requirements.
In one possible implementation, various behavior data of the user during the process of watching the video, such as whether the user finishes playing, praise, comment number, attention number of the video author, and the like, can be counted to obtain the user feedback quality score of the video.
In one possible implementation, the video is played, the number of comments, the attention of the video author is positive feedback, namely the addition; if the video playing time length is less than a certain set time length (e.g. 5 s), the negative feedback is taken as a negative feedback, namely the subtraction item. And calculating to obtain a user feedback score according to all the score adding items and score subtracting items obtained through statistics.
In one possible implementation, the foreground recommendation rule includes that the video frame contains a face and the user feedback quality score is above a preset user feedback quality threshold.
In the implementation manner, when the video picture contains a human face, the video is preliminarily determined to be suitable for foreground playing, the user feedback quality score corresponding to the video is further obtained, and if the user feedback quality score is higher than a preset user feedback quality threshold, the video is determined to accord with a foreground recommendation rule.
In another possible implementation, the foreground recommendation rules include: the video picture comprises a human face, the audio quality is not lower than a first preset audio quality threshold, and the user feedback quality score is higher than a preset user feedback quality threshold.
In the implementation manner, when the video picture contains a human face, the video is preliminarily determined to be suitable for foreground playing, the audio quality corresponding to the video is further obtained, if the audio quality is not lower than a first preset audio quality threshold, the user feedback quality score of the video is further obtained, and if the user feedback quality score is higher than the preset user feedback quality threshold, the video is finally determined to be in accordance with a foreground recommendation rule.
In yet another embodiment, the foreground recommendation rule includes at least that the picture quality is greater than or equal to a preset picture quality threshold. If the video picture quality is not lower than a preset picture quality threshold, determining that the video accords with a foreground recommendation rule; and if the video picture quality is lower than a preset picture quality threshold, determining that the video accords with the background recommendation rule.
The preset picture quality threshold can be freely set according to actual requirements. In addition, the picture quality can be evaluated from at least one dimension of whether a face is included, picture sharpness, and picture richness, for example, a face is included in a picture, and the picture is sharp and rich, the picture quality of such video is high.
In S240, when it is determined that the client is in the background running state, at least one second recommendation type video is acquired.
Wherein the at least one second recommendation type video meets a background recommendation rule.
When the client is in a background running state, the user is generally considered to not watch video pictures and only listen to audio content; therefore, at this time, video with higher audio quality can be recommended to the user to improve the viewing rate of the background user.
In one possible implementation, the background recommendation rules include at least: the audio quality of the video is greater than a second preset audio quality threshold, wherein the second preset audio quality threshold is greater than the first preset audio quality threshold.
In this implementation, whether or not the video frame contains a face, it is determined that the video meets the background recommendation rule as long as the audio quality of the video is greater than the second preset audio quality threshold. For example, taking a music video as an example, a video which contains a human face but has a single picture and is more suitable for listening to audio, such as a plain personal pop video, is recommended to the user.
In another possible implementation, the background recommendation rules include: the video frame does not contain a face and the audio quality is greater than a second preset audio quality threshold.
In this implementation manner, if the video picture does not contain a human face, the video is preliminarily determined to be suitable for background playing, the audio quality of the video is further determined, and if the audio quality is greater than a second preset audio quality threshold, the video is determined to accord with a background recommendation rule. For example, music video that does not show a face but has a higher performance level.
According to the video recommendation method provided by the embodiment, when the client plays the target type video, if the client is in the foreground running state, at least one first recommendation type video conforming to the foreground recommendation rule is recommended to the client. If the client is in the background running state, recommending at least one second recommendation type video meeting the background recommendation rule to the client. Therefore, the video matched with the corresponding running states is recommended for different running states of the foreground and the background respectively, and finally the accuracy of the recommended result is improved.
Fig. 3 is a flowchart of yet another video recommendation method according to an exemplary embodiment, which focuses on a process that a server obtains videos matching different recommendation rules, as shown in fig. 2, and includes the following steps.
In S310, when the server receives the target type video uploaded by the client, it is determined whether the video frame contains a face; if yes, then S320 is performed; if not, S350 is performed.
In one possible implementation, the target type video uploaded by any client is stored in a corresponding video queue in the server, and then the server reads the video from the video queue to evaluate the indexes such as audio quality, user feedback quality score and the like.
When a user uses a client to issue a video (i.e., the client sends the video to a server), the server stores the video sent by the client into a queue, and then reads the video from the queue to determine whether the video contains a face.
In S320, the audio quality of the video and the user feedback quality score are determined.
The process of determining the audio quality and the user feedback quality score is referred to in the relevant content of S230, and will not be described herein.
In S330, the video is stored in the first type video set, and the audio quality and user feedback quality score corresponding to the video are marked.
In one embodiment, when a video is stored in a first type of video set, the audio quality and user feedback quality score corresponding to the video are stored simultaneously.
In S340, when the server determines that the client is in the foreground running state, at least one video meeting the foreground recommendation rule is selected from the first video set and sent to the client.
In one embodiment, when selecting recommended videos from the first video set, selecting videos with audio quality not lower than a first preset audio threshold, and sending videos with user feedback quality scores higher than a preset user feedback quality score to the client.
In another embodiment, the preset number of videos may be selected and sent to the client directly according to the order of the audio quality and the user feedback quality score from high to low.
In S350, the audio quality of the video is determined, the video is stored in a second type of video set, and the audio quality of the video is marked.
In one embodiment, when a video is stored in the second video set, the corresponding audio quality of the video is stored at the same time.
In S360, when the server determines that the client is in the background running state, at least one video meeting the background recommendation rule is selected from the second video set and sent to the client.
In one embodiment, video with audio quality higher than a second preset audio quality threshold is selected from the second video set and sent to the client.
Wherein the second preset audio quality threshold is greater than the first preset audio quality threshold. If no face is contained in the video frame, continuing to determine the audio quality of the video. If the audio quality of the video is greater than the second preset audio quality threshold, this indicates that the audio quality of the video is higher, i.e., the video is more suitable for background playback.
In another embodiment, when selecting the video recommended to the user from the second video set, the preset number of videos are selected and sent to the client according to the order of the audio quality from high to low.
According to the video recommendation method provided by the embodiment, when a user uploads a target type video to a server, the server firstly determines whether an uploaded video picture contains a human face or not and is roughly divided into two types; aiming at the video containing the face in the video picture, the audio quality and the user feedback quality score of the video are further determined, and the video, the corresponding audio quality and the user feedback quality score are stored in a first video set. And if the client is in the foreground running state, selecting videos meeting the foreground recommendation rule from the first video set and sending the videos to the client. For the video which does not contain the face in the video picture, preliminarily determining that the video is suitable for background playing, further determining the audio quality of the video, and storing the video and the corresponding audio quality into a second type video set. And if the client is in the background running state, selecting videos meeting background recommendation rules from the second type of video set and sending the videos to the client. According to the scheme, the high-quality video suitable for the running state can be recommended for the users in the foreground and background running states respectively, and the accuracy of the video recommendation result is improved. In addition, when the user uploads the video, the method evaluates and records the indexes of the uploaded video in each evaluation dimension, so that the video is directly recommended according to the recorded indexes when the video is subsequently recommended to the user, the response time of the server side when the video is recommended is shortened, and the recommendation speed is improved.
Corresponding to the video recommending method embodiment, the present disclosure further provides a video recommending apparatus embodiment.
FIG. 4 is a block diagram illustrating a video recommendation device, according to an exemplary embodiment, applied to a client, as shown in FIG. 4, the device comprising: a video type determination module 110, an operational status acquisition module 120, and a recommended video acquisition module 130.
The video type determining module 110 is configured to determine a video type to be played by the client.
In one application scenario, the video type determination module 110 includes: the first determining submodule is configured to respond to detection of selection operation of selecting a video type to be played on the client by a user, and determine the video type corresponding to the selection operation as the video type to be played by the client.
In another application scenario, the video type determination module 110 includes: and the second determining submodule is configured to determine the same video type as the video type to be played by the client when the video type played by the client in a preset time period before the current moment belongs to the same video type.
An operation state obtaining module 120, configured to obtain an operation state of the client when the video type is determined to belong to a target type.
The recommended video obtaining module 130 is configured to obtain at least one recommended type video according to the running state of the client, where the at least one recommended type video belongs to the target type and matches with the running state of the client.
In one embodiment, as shown in FIG. 4, the recommended video acquisition module 130 includes:
the first obtaining sub-module 131 is configured to obtain at least one first recommendation type video when it is determined that the client is in a foreground running state, where the at least one first recommendation type video meets a foreground recommendation rule matched with the foreground running state.
In one possible implementation, the foreground recommendation rules include at least: the video picture contains a human face.
In another possible implementation, the foreground recommendation rule at least further includes: the audio quality of the video is not lower than a first preset audio quality threshold.
In yet another possible implementation, the foreground recommendation rule at least further includes: the user feedback quality score of the video is higher than a preset user feedback quality threshold, and the user feedback quality score is obtained according to user behaviors generated when the user watches the video.
In another embodiment, as shown in fig. 5, the recommended video acquisition module 130 further includes:
the second obtaining sub-module 132 is configured to obtain at least one second recommendation type video when the client is determined to be in the background running state, where the at least one second recommendation type video meets a background recommendation rule matched with the background running state.
In one possible implementation, the background recommendation rules include at least: the audio quality of the video is greater than a second preset audio quality threshold, which is greater than the first preset audio quality threshold.
According to the video recommending apparatus provided by the embodiment, the video type to be played by the client is determined, if the video type belongs to the target type, the running state of the client is obtained, and at least one recommended type video is obtained according to the running state of the client, wherein the recommended type video belongs to the target type and is matched with the running state of the client. For example, the running states of the client include a foreground running state and a background running state, and if the client is in the foreground running state, a recommended type video matched with the foreground running state is acquired; and if the client is in the background running state, acquiring the recommended type video matched with the background running state. According to the scheme, when the video is recommended to the user, the recommended video matched with the running state of the client can be recommended, so that the matching degree between the recommended video and the running state of the client is improved, and finally the accuracy of video recommendation is improved.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 6 is a block diagram of an electronic device, such as a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc., according to an example embodiment.
Referring to fig. 6, an electronic device 600 may include one or more of the following components: a processing component 602, a memory 604, a power component 606, a multimedia component 608, an audio component 610, an input/output (I/O) interface 612, a sensor component 614, and a communication component 616.
The processing component 602 generally controls overall operation of the electronic device 600, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 602 may include one or more processors 620 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 602 can include one or more modules that facilitate interaction between the processing component 602 and other components. For example, the processing component 602 may include a multimedia module to facilitate interaction between the multimedia component 608 and the processing component 602.
The memory 604 is configured to store various types of data to support operations at the electronic device 600. Examples of such data include instructions for any application or method operating on the electronic device 600, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 604 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power supply component 606 provides power to the various components of the electronic device 600. The power supply components 606 can include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 600.
The multimedia component 608 includes a screen between the electronic device 600 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 608 includes a front camera and/or a rear camera. When the electronic device 600 is in an operational mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 610 is configured to output and/or input audio signals. For example, the audio component 610 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 600 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 604 or transmitted via the communication component 616. In some embodiments, audio component 610 further includes a speaker for outputting audio signals.
The I/O interface 612 provides an interface between the processing component 602 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 614 includes one or more sensors for providing status assessment of various aspects of the electronic device 600. For example, the sensor assembly 614 may detect an on/off state of the electronic device 600, a relative positioning of the components, such as a display and keypad of the electronic device 600, the sensor assembly 614 may also detect a change in position of the electronic device 600 or a component of the electronic device 600, the presence or absence of a user's contact with the electronic device 600, an orientation or acceleration/deceleration of the electronic device 600, and a change in temperature of the electronic device 600. The sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. The sensor assembly 614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 616 is configured to facilitate communication between the electronic device 600 and other devices, either wired or wireless. The electronic device 600 may access a wireless network based on a communication standard, such as WiFi, an operator network (e.g., 3G, 4G, 5G, etc.), or a combination thereof. In one exemplary embodiment, the communication component 616 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 616 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 600 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for performing the video recommendation method described above.
In an exemplary embodiment, a computer readable storage medium is also provided, such as storage 604, comprising instructions executable by processor 620 of electronic device 600 to perform the video recommendation method described above. Alternatively, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
In an exemplary embodiment, a computer program product is also provided having instructions stored therein that when executed by a processor in an electronic device, implement any of the video recommendation methods described above.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (15)
1. A video recommendation method, the method comprising:
determining the type of the video to be played by the client;
when the video type is determined to belong to a target type, acquiring the running state of the client;
Acquiring at least one recommended type video according to the running state of the client, wherein the at least one recommended type video belongs to the target type and is matched with the running state of the client;
the obtaining at least one recommended type video according to the running state of the client comprises:
when the client is determined to be in a foreground running state, acquiring at least one first recommendation type video, wherein the at least one first recommendation type video accords with a foreground recommendation rule matched with the foreground running state;
and when the client is determined to be in a background running state, acquiring at least one second recommendation type video, wherein the at least one second recommendation type video accords with a background recommendation rule matched with the background running state.
2. The video recommendation method according to claim 1, wherein the foreground recommendation rule includes at least: the video picture contains a human face.
3. The video recommendation method according to claim 2, wherein the foreground recommendation rule further comprises at least: the audio quality of the video is not lower than a first preset audio quality threshold.
4. The video recommendation method according to claim 2, wherein the foreground recommendation rule further comprises at least: the user feedback quality score of the video is higher than a preset user feedback quality threshold, and the user feedback quality score is obtained according to user behaviors generated when the user watches the video.
5. The video recommendation method according to claim 3, wherein the background recommendation rule at least includes: the audio quality of the video is greater than a second preset audio quality threshold, which is greater than the first preset audio quality threshold.
6. The video recommendation method according to claim 1, wherein the determining the type of video to be played by the client comprises:
in response to detecting a selection operation of selecting a video type to be played on the client by a user, determining the video type corresponding to the selection operation as the video type to be played on the client;
or, the determining the video type to be played by the client includes:
and when the video types played by the client in the preset time period before the current moment belong to the same video type, determining the same video type as the video type to be played by the client.
7. A video recommendation device, the device comprising:
the video type determining module is configured to determine the type of the video to be played by the client;
the running state acquisition module is configured to acquire the running state of the client when the video type is determined to belong to a target type;
The recommended video acquisition module is configured to acquire at least one recommended type video according to the running state of the client, wherein the at least one recommended type video belongs to the target type and is matched with the running state of the client;
the recommended video acquisition module comprises:
the first acquisition sub-module is configured to acquire at least one first recommendation type video when the client is determined to be in a foreground running state, wherein the at least one first recommendation type video accords with a foreground recommendation rule matched with the foreground running state;
and the second acquisition sub-module is configured to acquire at least one second recommendation type video when the client is determined to be in a background running state, wherein the at least one second recommendation type video accords with a background recommendation rule matched with the background running state.
8. The video recommendation device of claim 7, wherein the foreground recommendation rules comprise at least: the video picture contains a human face.
9. The video recommendation device of claim 8, wherein the foreground recommendation rules further comprise at least: the audio quality of the video is not lower than a first preset audio quality threshold.
10. The video recommendation device of claim 8, wherein the foreground recommendation rules further comprise at least: the user feedback quality score of the video is higher than a preset user feedback quality threshold, and the user feedback quality score is obtained according to user behaviors generated when the user watches the video.
11. The video recommendation device of claim 9, wherein the background recommendation rules comprise at least: the audio quality of the video is greater than a second preset audio quality threshold, which is greater than the first preset audio quality threshold.
12. The video recommendation device of claim 7, wherein the video type determination module comprises:
the first determining submodule is configured to respond to detection of selection operation of selecting a video type to be played on the client by a user, and determine the video type corresponding to the selection operation as the video type to be played by the client.
13. The video recommendation device of claim 7, wherein the video type determination module comprises:
and the second determining submodule is configured to determine the same video type as the video type to be played by the client when the video type played by the client in a preset time period before the current moment belongs to the same video type.
14. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the video recommendation method of any one of claims 1 to 6.
15. A computer readable storage medium having instructions stored thereon, which when executed by a processor of an electronic device, cause the electronic device to perform the video recommendation method of any of claims 1 to 6.
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| CN115022654B (en) * | 2022-05-18 | 2024-01-19 | 北京达佳互联信息技术有限公司 | Video editing method and device in live broadcast scene |
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