CN114143612A - Video display method, video display device, electronic equipment, storage medium and program product - Google Patents
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- H—ELECTRICITY
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/472—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content
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- H—ELECTRICITY
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
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Abstract
The disclosure provides a video display method, a video display device, an electronic apparatus, a storage medium and a program product. The video display method comprises the following steps: responding to a video playing request of a first target video in a video to be played, and acquiring the video playing time of the first target video; determining the playing type of a first target video according to the video playing duration; updating the prediction result of each second target video belonging to the playing type according to the characteristics of each second target video and the characteristics of the latest preset number of played videos belonging to the playing type aiming at a plurality of second target videos behind the first target video in the videos to be played; updating the final prediction result of each second target video according to the updated prediction result of each second target video belonging to the playing type; and sequencing and displaying the plurality of second target videos according to the updated final prediction result of each second target video. According to the method and the device, the instant requirements of the user can be better met.
Description
Technical Field
The present disclosure relates generally to the field of electronics, and more particularly, to a video presentation method, apparatus, electronic device, storage medium, and program product.
Background
In order to reduce the server pressure and reduce the interaction times between the client and the server, the server generally issues a plurality of videos to the client at one time, and the client displays the videos to the user in sequence. However, this approach ignores the instant requirements of the user, resulting in the delivered video ranking not matching the real-time interest change of the user.
Disclosure of Invention
Exemplary embodiments of the present disclosure are directed to a video presentation method, apparatus, electronic device, storage medium, and program product for solving at least the problems of the related art described above.
According to a first aspect of the embodiments of the present disclosure, there is provided a video display method, including: responding to a video playing request of a first target video in videos to be played by a target account, and acquiring the video playing time of the first target video; determining the playing type of the first target video according to the video playing duration; updating the prediction result of each second target video belonging to the playing type according to the characteristics of each second target video and the characteristics of a preset number of played videos belonging to the playing type for a plurality of second target videos behind the first target video in the videos to be played; updating the final prediction result of each second target video according to the updated prediction result of each second target video belonging to the playing type; and sequencing and displaying the plurality of second target videos according to the updated final prediction result of each second target video.
Optionally, the step of updating the prediction result of each second target video belonging to the play type according to the feature of each second target video and the feature of a predetermined number of recently played videos belonging to the play type includes: when the playing type is long-distance playing, inputting the characteristics of each second target video and the characteristics of the latest first preset number of played videos belonging to the long-distance playing type into a first prediction model to obtain a long-distance playing score of each second target video, wherein the long-distance playing score represents the possibility that the videos can be long-distance played; and when the playing type is non-long-play, inputting the characteristics of each second target video and the characteristics of a second preset number of recently played videos belonging to the non-long-play type into a second prediction model to obtain a non-long-play score of each second target video, wherein the non-long-play score represents the possibility that the video cannot be long-played.
Optionally, the step of inputting the features of each second target video and the features of the latest first predetermined number of played videos belonging to the long play type into the first prediction model to obtain the long play score of each second target video includes: inputting the characteristics of each second target video and the characteristics of the latest first preset number of played videos belonging to the long-play type into a first attention network to obtain the attention result of each played video in the latest first preset number of played videos belonging to the long-play type about the second target video; taking the attention result of each played video as a weighted value of the characteristics of the played video, and carrying out weighted summation operation on the characteristics of the latest first preset number of played videos belonging to the long play type; inputting the weighted sum operation result and the characteristics of the second target video into a first splicing layer; and inputting the vector output by the first splicing layer into the first classification layer after passing through the first full connection layer to obtain the long-play score of the second target video output by the first classification layer.
Optionally, the step of inputting the features of each second target video and the features of the latest second predetermined number of played videos belonging to the non-long-play type into the second prediction model to obtain the non-long-play score of each second target video includes: inputting the characteristics of each second target video and the characteristics of the latest second preset number of played videos belonging to the non-long-play type into a second attention network to obtain the attention result of each played video in the latest second preset number of played videos belonging to the non-long-play type, wherein the attention result of each played video is related to the second target video; taking the attention result of each played video as a weighted value of the characteristics of the played video, and carrying out weighted summation operation on the characteristics of the latest second preset number of played videos belonging to the non-long-play type; inputting the weighted sum operation result and the characteristics of the second target video into a second splicing layer; and inputting the vector output by the second splicing layer into the second classification layer after passing through the second full connection layer to obtain the non-long-play score of the second target video output by the second classification layer.
Optionally, the final prediction result of each second target video is a difference between the long-run score and the non-long-run score of the second target video.
Optionally, the video display method further includes: receiving the video to be played and the characteristics thereof sent by a server; and receiving the trained first prediction model and the second prediction model sent by the server.
Optionally, the higher the final prediction result of the second target video, the higher the ranking.
According to a second aspect of the embodiments of the present disclosure, there is provided a video presentation apparatus, comprising: the playing time length obtaining unit is configured to respond to a video playing request of a first target video in videos to be played by a target account, and obtain the video playing time length of the first target video; a play type determining unit configured to determine a play type of the first target video according to the video play duration; a play type prediction unit configured to update, for a plurality of second target videos subsequent to the first target video in the video to be played, a prediction result of each second target video belonging to the play type according to a feature of each second target video and a feature of a predetermined number of played videos belonging to the play type at the latest; a final prediction result obtaining unit configured to update a final prediction result of each second target video according to the updated prediction result of each second target video belonging to the play type; and the sequencing display unit is configured to sequence and display the plurality of second target videos according to the updated final prediction result of each second target video.
Optionally, the play type prediction unit is configured to: when the playing type is long-distance playing, inputting the characteristics of each second target video and the characteristics of the latest first preset number of played videos belonging to the long-distance playing type into a first prediction model to obtain a long-distance playing score of each second target video, wherein the long-distance playing score represents the possibility that the videos can be long-distance played; and when the playing type is non-long-play, inputting the characteristics of each second target video and the characteristics of a second preset number of recently played videos belonging to the non-long-play type into a second prediction model to obtain a non-long-play score of each second target video, wherein the non-long-play score represents the possibility that the video cannot be long-played.
Optionally, the play type prediction unit is configured to: when the playing type is long-distance playing, inputting the characteristics of each second target video and the characteristics of the latest first preset number of played videos belonging to the long-distance playing type into a first attention network to obtain the attention result of each played video in the latest first preset number of played videos belonging to the long-distance playing type relative to the second target video; taking the attention result of each played video as a weighted value of the characteristics of the played video, and carrying out weighted summation operation on the characteristics of the latest first preset number of played videos belonging to the long play type; inputting the weighted sum operation result and the characteristics of the second target video into a first splicing layer; and inputting the vector output by the first splicing layer into the first classification layer after passing through the first full connection layer to obtain the long-play score of the second target video output by the first classification layer.
Optionally, the play type prediction unit is configured to: when the playing type is non-long-play, inputting the characteristics of each second target video and the characteristics of the latest second preset number of played videos belonging to the non-long-play type into a second attention network to obtain the attention result of each played video in the latest second preset number of played videos belonging to the non-long-play type, wherein the attention result of each played video is related to the second target video; taking the attention result of each played video as a weighted value of the characteristics of the played video, and carrying out weighted summation operation on the characteristics of the latest second preset number of played videos belonging to the non-long-play type; inputting the weighted sum operation result and the characteristics of the second target video into a second splicing layer; and inputting the vector output by the second splicing layer into the second classification layer after passing through the second full connection layer to obtain the non-long-play score of the second target video output by the second classification layer.
Optionally, the final prediction result of each second target video is a difference between the long-run score and the non-long-run score of the second target video.
Optionally, the video display apparatus further comprises: the receiving unit is configured to receive the video to be played and the characteristics thereof sent by the server; and receiving the trained first prediction model and the second prediction model sent by the server.
Optionally, the higher the final prediction result of the second target video, the higher the ranking.
According to a third aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including: at least one processor; at least one memory storing computer-executable instructions, wherein the computer-executable instructions, when executed by the at least one processor, cause the at least one processor to perform the video presentation method as described above.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium, wherein instructions, when executed by at least one processor, cause the at least one processor to perform the video presentation method as described above.
According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product comprising computer instructions which, when executed by at least one processor, implement the video presentation method as described above.
According to the exemplary embodiment of the disclosure, the real-time demand of the user can be captured through the real-time consumption condition of the video by the user, and the video display sequence is adjusted according to the real-time demand of the user, so that the displayed video can be matched with the real-time interest change of the user, the real-time demand of the user can be met, and better user experience is achieved.
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 present disclosure and, together with the description, serve to explain the principles of the disclosure and are not to be construed as limiting the disclosure.
Fig. 1 illustrates an application scene diagram of a video presentation method according to an exemplary embodiment of the present disclosure;
fig. 2 illustrates a flow chart of a video presentation method according to an exemplary embodiment of the present disclosure;
fig. 3 illustrates a flowchart of a method of deriving a long play score for a video to be played according to an exemplary embodiment of the present disclosure;
fig. 4 illustrates a flowchart of a method of deriving a non-longcast score for a video to be broadcast, according to an exemplary embodiment of the present disclosure;
fig. 5 illustrates a block diagram of a video presentation apparatus according to an exemplary embodiment of the present disclosure;
fig. 6 illustrates a block diagram of an electronic device according to an exemplary embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in 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 above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
In this case, the expression "at least one of the items" in the present disclosure means a case where three types of parallel expressions "any one of the items", "a combination of any plural ones of the items", and "the entirety of the items" are included. For example, "include at least one of a and B" includes the following three cases in parallel: (1) comprises A; (2) comprises B; (3) including a and B. For another example, "at least one of the first step and the second step is performed", which means that the following three cases are juxtaposed: (1) executing the step one; (2) executing the step two; (3) and executing the step one and the step two.
Fig. 1 illustrates an example of an application scene of a video presentation method according to an exemplary embodiment of the present disclosure.
Referring to fig. 1, when a user needs to watch a video at a client, the client may send a video acquisition request to a server; the server side can respond to the video acquisition request of the client side and send a plurality of videos to be played and the characteristics of the videos to be played to the client side; the client can sequence the videos to be played, display the videos to be played according to the sequencing result, execute the video display method according to the exemplary embodiment of the disclosure in the process of displaying the videos to be played, and obtain the video playing time length of a first target video in the videos to be played in response to a video playing request of the first target video from a target account; determining the playing type of the first target video according to the video playing duration; updating the prediction result of each second target video belonging to the playing type according to the characteristics of each second target video and the characteristics of a preset number of played videos belonging to the playing type for a plurality of second target videos behind the first target video in the videos to be played; updating the final prediction result of each second target video according to the updated prediction result of each second target video belonging to the playing type; and sequencing and displaying the plurality of second target videos according to the updated final prediction result of each second target video. Therefore, the displayed video can be more matched with the real-time interest change of the user, and the instant requirement of the user is met.
It should be understood that the video presentation method according to the exemplary embodiment of the present disclosure may be applied not only to the above-described scenes but also to other suitable scenes, and the present disclosure is not limited thereto.
Fig. 2 illustrates a flowchart of a video presentation method according to an exemplary embodiment of the present disclosure. As an example, the video presentation method may be performed by a client.
Referring to fig. 2, in step S101, in response to a video playing request of a first target video in a video to be played by a target account, a video playing duration of the first target video is obtained.
Specifically, the first target video may be played in response to a video playing request of a first target video in the videos to be played by the target account, and the video playing time length of the first target video may be obtained. For example, the video playing time length of the first target video may be the total time length of the first target video that has been played when the playing of the first target video is finished.
In step S102, the play type of the first target video is determined according to the video play duration.
By way of example, the playback types of the video may include: long-length broadcast and non-long-length broadcast. It should be understood that the playing type of the video can also be divided from other dimensions based on the duration of the video, and the present disclosure is not limited thereto.
As an example, the playback with the playback time length exceeding the preset threshold belongs to the long-play type, otherwise belongs to the non-long-play type.
In step S103, for a plurality of second target videos subsequent to the first target video in the video to be played, the prediction result of each second target video belonging to the play type is updated according to the feature of each second target video and the features of a predetermined number of played videos belonging to the play type.
As an example, the video may be characterized as: embedding feature of video. It should be understood that other types of features are possible, and the disclosure is not limited thereto.
As an example, a prediction that a video is of a long-run type characterizes the likelihood that the video will be long-run. For example, the prediction result that a video belongs to a long-run type may be represented in the form of a score (which may be referred to as a long-run score, for example), the higher the likelihood that the video will be long-run, the higher the long-run score. The video will be long-cast as: the playing time for playing the video exceeds the preset threshold.
As an example, a prediction that a video is of a non-long-play type characterizes the likelihood that the video will not be long-played. For example, the prediction result that a video belongs to a non-long-run type may be represented in the form of a score (e.g., may be referred to as a non-long-run score), the higher the likelihood that the video will not be long-run, the higher the non-long-run score. Video is not long-cast as: the playing time for playing the video does not exceed the preset threshold.
In step S104, the final prediction result of each second target video is updated according to the updated prediction result that each second target video belongs to the play type.
As an example, when the play type is long-play, the characteristics of each second target video and the characteristics of the latest first predetermined number of played videos belonging to the long-play type may be input into the first prediction model, and the long-play score of the second target video is obtained as an updated prediction result that the second target video belongs to the long-play type. The long play score indicates the likelihood that the video will be long played.
As an example, when the play type is non-long play, the feature of each second target video and the features of the latest second predetermined number of played videos belonging to the non-long play type may be input into the second prediction model, and the non-long play score of the second target video is obtained as an updated prediction result that the second target video belongs to the non-long play type. The non-longcast score represents the likelihood that the video will not be longcast.
As an example, the final prediction result of each video may be obtained based on the prediction results of the videos respectively belonging to the respective play types. For example, when the playback types include long-play and non-long-play, the final prediction result for each video may be obtained based on the prediction result of the video belonging to the long-play type and the prediction result belonging to the non-long-play type. For example, it may be a difference between a prediction result belonging to a long-cast type and a prediction result belonging to a non-long-cast type. For example, the final prediction result of a video may be the difference (m-n) of the predicted long play fraction m and the non-long play fraction n of the video.
Specifically, after the prediction result of each second target video belonging to the play type is updated, the final prediction result of the second target video needs to be updated according to the updated prediction result of the second target video belonging to the play type. For example, when the play type is long-play, the final prediction result of the second target video is obtained as the final prediction result of the updated second target video according to the updated prediction result that the second target video belongs to the long-play type and the latest prediction result that the second target video belongs to the non-long-play type.
As an example, the first predictive model may include: a first attention attribution network, a first splice concat layer, a first fully connected layer, and a first classification layer. It should be understood that this model structure is merely an example, and the first predictive model may be constructed in other forms as well, and the present disclosure is not limited thereto. For example, the first fully-connected layer may comprise a two-layer fc network. For example, the first classification layer may use a softmax function. An exemplary embodiment of step S103 will be described below in conjunction with fig. 3.
As an example, the second predictive model may include: a second attention network, a second splice layer, a second fully connected layer, and a second classification layer. It should be understood that this model structure is merely an example, and the second predictive model may be constructed in other forms as well, and the disclosure is not limited thereto. For example, the second fully-connected layer may comprise a two-layer fc network. For example, the second classification layer may use a softmax function. An exemplary embodiment of step S103 will be described below in conjunction with fig. 4.
In step S105, the plurality of second target videos are sorted and displayed according to the updated final prediction result of each second target video.
Specifically, the second target videos are reordered according to the updated final prediction result of each second target video and displayed according to the ordering result. That is, the more top the ranking, the more preferred the ranking is presented.
As an example, the higher the final prediction result of the second target video, the earlier the ranking.
As an example, the video presentation method according to an exemplary embodiment of the present disclosure may further include: and receiving the video to be played and the characteristics thereof sent by the server. For example, N videos to be played and characteristics of each video to be played, which are delivered by the server side, may be received. Here, N is an integer greater than 0. For example, N may be an integer greater than or equal to 8. As an example, when a video to be broadcasted that is delivered by a server is received, the characteristics of each video to be broadcasted and the characteristics of a first predetermined number of videos that have been broadcasted and belong to a long broadcast type are input to a first prediction model, so as to obtain an initial long broadcast score of each video to be broadcasted; inputting the characteristics of each video to be played and the characteristics of a second preset number of played videos which belong to the non-long-play type to a second prediction model to obtain an initial non-long-play score of each video to be played; then, obtaining a final prediction result of the video to be played based on the long-play score and the non-long-play score of each video to be played; and then, according to the final prediction result of each video to be played, initially sequencing and displaying the videos to be played.
According to an exemplary embodiment of the present disclosure, after the videos to be played are initially sorted and displayed, after each time one displayed video to be played (i.e., a first target video) is long-played, inputting the features of each video to be played (i.e., a second target video) ranked after the long-played video and the features of the latest first predetermined number of videos already played belonging to the long-played type into the first prediction model, and obtaining a long-played score (i.e., an updated long-played score) of each second target video which is re-predicted; then, updating the final prediction result of each second target video based on the original non-long-play score and the re-predicted long-play score of each second target video; and then, sequencing and displaying the plurality of second target videos according to the updated final prediction result of each second target video. In other words, each time the user consumes (i.e., watches) one of the videos P to be shown, if the video P is long-cast, the long-cast score of the video to be cast that is ranked after the video P needs to be updated because the sequence of the latest first predetermined number of already-cast videos belonging to the long-cast type has changed: the video P is added to the sequence and accordingly one of the original videos in the sequence is topped. Therefore, the final prediction result of the subsequent video to be played can be influenced, the subsequent video to be played is reordered according to the real-time updated final prediction result, and is sequentially displayed to the user.
According to the exemplary embodiment of the present disclosure, after the videos to be played are initially sorted and displayed, after each time one displayed video to be played (i.e., the first target video) is not long-played, the features of each video to be played (i.e., the second target video) ranked behind the video and the features of the latest second predetermined number of videos already played belonging to the non-long-played type are input into the second prediction model, so as to obtain a non-long-played score (i.e., an updated non-long-played score) of each second target video which is re-predicted; then, updating the final prediction result of each second target video based on the original long-play score and the re-predicted non-long-play score of each second target video; and then, sequencing and displaying the plurality of second target videos according to the updated final prediction result of each second target video. In other words, each time the user consumes (i.e., watches) one of the videos Q to be shown, if the video Q is not long-cast, the non-long-cast score of the video to be cast that is ranked after the video Q needs to be updated because the sequence of the latest second predetermined number of already-cast videos belonging to the non-long-cast type has changed: video Q is added to the sequence and accordingly one of the original videos in the sequence is topped off. Therefore, the final prediction result of the subsequent video to be played can be influenced, the subsequent video to be played is reordered according to the real-time updated final prediction result, and is sequentially displayed to the user.
As an example, the video presentation method according to an exemplary embodiment of the present disclosure may further include: and receiving the trained first prediction model and the second prediction model sent by the server. For example, after the trained first and second prediction models are received for the first time, updated model parameters of the first prediction model may be received each time the first prediction model is updated; whenever the second predictive model is updated, updated model parameters of the second predictive model are received.
Fig. 3 shows a flowchart of a method for obtaining a long play score of a video to be played according to an exemplary embodiment of the present disclosure. Here, the first prediction model includes: a first attention network, a first splice layer, a first fully connected layer, and a first classification layer.
Referring to fig. 3, in step S1011, the characteristics of each second target video and the characteristics of the latest first predetermined number of played videos belonging to the long play type are input into the first attention network, and the attention result of each of the latest first predetermined number of played videos belonging to the long play type with respect to the second target video is obtained.
Specifically, the first attention network may combine, through an attention mechanism, the characteristics of each of the latest first predetermined number of already-played videos belonging to the long-run type and the characteristics of the second target video to obtain an attention result of each of the latest first predetermined number of already-played videos belonging to the long-run type with respect to the second target video.
In step S1012, a weighted summation operation is performed on the features of the latest first predetermined number of the played videos belonging to the long play type, with the attention result of each played video as a weighted value of the features of the played video.
Specifically, the attention result of each played video is multiplied by the characteristics of the played video to obtain a product result, and the product results corresponding to the latest first preset number of played videos belonging to the long play type are summed.
In step S1013, the weighted sum operation result and the feature of the second target video are input to the first mosaic layer.
That is, concat is performed on the feature of the second target video and the result of the weighted sum operation.
In step S1014, the vector output by the first splicing layer is input into the first classification layer after passing through the first full-link layer, so as to obtain the long-cast score of the second target video output by the first classification layer.
As an example, the first prediction model may be trained using a cross entropy loss function. It should be understood that other types of loss functions are possible, and the present disclosure is not limited thereto.
Fig. 4 shows a flowchart of a method of deriving a non-longcast score of a video to be broadcast according to an example embodiment of the present disclosure. The second predictive model may include: a second attention network, a second splice layer, a second fully connected layer, and a second classification layer.
Referring to fig. 4, in step S1021, the characteristics of each second target video and the characteristics of the latest second predetermined number of played videos belonging to the non-long play type are input into the second attention network, and the attention result of each played video of the latest second predetermined number of played videos belonging to the non-long play type with respect to the second target video is obtained.
In step S1022, a weighted summation operation is performed on the features of the latest second predetermined number of played videos belonging to the non-long-play type, using the attention result of each played video as a weighted value of the features of the played video.
In step S1023, the weighted sum operation result and the characteristics of the second target video are input to the second mosaic layer.
In step S1024, the vector output by the second splicing layer is input into the second classification layer after passing through the second full-link layer, so as to obtain the non-long-play score of the second target video output by the second classification layer.
As an example, the second prediction model may be trained using a cross entropy loss function. It should be understood that other types of loss functions are possible, and the present disclosure is not limited thereto.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
the client can sequence a plurality of videos issued to the client and display the videos according to the sequencing result, so that the computing capacity of the client is fully utilized, and the pressure of the server is reduced;
in the process of sequencing videos, the real-time consumption condition of the videos captured in real time by the user can be used, so that the sequencing result is more matched with the real-time interest change of the user, the instant requirement of the user can be better met, and better user experience is achieved;
by adopting the feedback network, the positive feedback of the user to the video can be captured in real time, the negative feedback of the user to the video can be captured in real time, the sequencing of the video transmitted to the client side can be adjusted in real time through the positive feedback and the negative feedback, the user requirements can be better met, and the user experience is well improved.
Fig. 5 illustrates a block diagram of a video presentation apparatus according to an exemplary embodiment of the present disclosure.
As shown in fig. 5, the video presentation apparatus 10 according to an exemplary embodiment of the present disclosure includes: a playing time length obtaining unit 101, a playing type determining unit 102, a playing type predicting unit 103, a final prediction result obtaining unit 104, and a sequencing display unit 105.
Specifically, the playing time length obtaining unit 101 is configured to obtain a video playing time length of a first target video in videos to be played in response to a video playing request of the first target video from a target account.
The play type determining unit 102 is configured to determine a play type of the first target video according to the video play duration.
The play type prediction unit 103 is configured to update, for a plurality of second target videos subsequent to the first target video in the video to be played, a prediction result that each second target video belongs to the play type according to a feature of each second target video and a feature of a predetermined number of played videos that belong to the play type at the latest.
The final prediction result obtaining unit 104 is configured to update the final prediction result of each second target video according to the updated prediction result of each second target video belonging to the play type.
The ranking presentation unit 105 is configured to rank and present the plurality of second target videos according to the updated final prediction result of each second target video.
As an example, the play type prediction unit 103 may be configured to: when the playing type is long-distance playing, inputting the characteristics of each second target video and the characteristics of the latest first preset number of played videos belonging to the long-distance playing type into a first prediction model to obtain a long-distance playing score of each second target video, wherein the long-distance playing score represents the possibility that the videos can be long-distance played; and when the playing type is non-long-play, inputting the characteristics of each second target video and the characteristics of a second preset number of recently played videos belonging to the non-long-play type into a second prediction model to obtain a non-long-play score of each second target video, wherein the non-long-play score represents the possibility that the video cannot be long-played.
As an example, the play type prediction unit 103 may be configured to: when the playing type is long-distance playing, inputting the characteristics of each second target video and the characteristics of the latest first preset number of played videos belonging to the long-distance playing type into a first attention network to obtain the attention result of each played video in the latest first preset number of played videos belonging to the long-distance playing type relative to the second target video; taking the attention result of each played video as a weighted value of the characteristics of the played video, and carrying out weighted summation operation on the characteristics of the latest first preset number of played videos belonging to the long play type; inputting the weighted sum operation result and the characteristics of the second target video into a first splicing layer; and inputting the vector output by the first splicing layer into the first classification layer after passing through the first full connection layer to obtain the long-play score of the second target video output by the first classification layer.
As an example, the play type prediction unit 103 may be configured to: when the playing type is non-long-play, inputting the characteristics of each second target video and the characteristics of the latest second preset number of played videos belonging to the non-long-play type into a second attention network to obtain the attention result of each played video in the latest second preset number of played videos belonging to the non-long-play type, wherein the attention result of each played video is related to the second target video; taking the attention result of each played video as a weighted value of the characteristics of the played video, and carrying out weighted summation operation on the characteristics of the latest second preset number of played videos belonging to the non-long-play type; inputting the weighted sum operation result and the characteristics of the second target video into a second splicing layer; and inputting the vector output by the second splicing layer into the second classification layer after passing through the second full connection layer to obtain the non-long-play score of the second target video output by the second classification layer.
As an example, the final prediction result for each second target video may be the difference between the long run score and the non-long run score of the second target video.
As an example, the video display apparatus 10 may further include: a receiving unit (not shown), configured to receive the video to be played and the characteristics thereof sent by the server; and receiving the trained first prediction model and the second prediction model sent by the server.
As an example, the higher the final prediction result of the second target video, the earlier the ranking.
With respect to the video presenter 10 of the above-described embodiment, the specific manner in which each unit performs operations has been described in detail in the embodiment related to the method, and will not be described in detail here.
Further, it should be understood that the various units in the video presentation apparatus 10 in the above embodiments may be implemented as hardware components and/or software components. The individual units may be implemented, for example, using Field Programmable Gate Arrays (FPGAs) or Application Specific Integrated Circuits (ASICs), depending on the processing performed by the individual units as defined by the skilled person.
Fig. 6 illustrates a block diagram of an electronic device according to an exemplary embodiment of the present disclosure. Referring to fig. 6, the electronic device 20 includes: at least one memory 201 and at least one processor 202, the at least one memory 201 having stored therein a set of computer-executable instructions that, when executed by the at least one processor 202, perform a video presentation method as described in the above exemplary embodiments.
By way of example, the electronic device 20 may be a PC computer, tablet device, personal digital assistant, smart phone, or other device capable of executing the set of instructions described above. The electronic device 20 need not be a single electronic device, but can be any collection of devices or circuits that can execute the above instructions (or sets of instructions) individually or in combination. The electronic device 20 may also be part of an integrated control system or system manager, or may be configured as a portable electronic device that interfaces with local or remote (e.g., via wireless transmission).
In the electronic device 20, the processor 202 may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a programmable logic device, a special purpose processor system, a microcontroller, or a microprocessor. By way of example, and not limitation, processor 202 may also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, or the like.
The processor 202 may execute instructions or code stored in the memory 201, wherein the memory 201 may also store data. The instructions and data may also be transmitted or received over a network via a network interface device, which may employ any known transmission protocol.
In addition, the electronic device 20 may also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, mouse, touch input device, etc.). All components of the electronic device 20 may be connected to each other via a bus and/or a network.
According to an exemplary embodiment of the present disclosure, there may also be provided a computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform the video presentation method as described in the above exemplary embodiment. Examples of the computer-readable storage medium herein include: read-only memory (ROM), random-access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random-access memory (DRAM), static random-access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD + R, CD-RW, CD + RW, DVD-ROM, DVD-R, DVD + R, DVD-RW, DVD + RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or compact disc memory, Hard Disk Drive (HDD), solid-state drive (SSD), card-type memory (such as a multimedia card, a Secure Digital (SD) card or a extreme digital (XD) card), magnetic tape, a floppy disk, a magneto-optical data storage device, an optical data storage device, a hard disk, a magnetic tape, a magneto-optical data storage device, a hard disk, a magnetic tape, a magnetic data storage device, a magnetic tape, a magnetic data storage device, a magnetic tape, a magnetic data storage device, a magnetic tape, a magnetic data storage device, a magnetic tape, a magnetic data storage device, A solid state disk, and any other device configured to store and provide a computer program and any associated data, data files, and data structures to a processor or computer in a non-transitory manner such that the processor or computer can execute the computer program. The computer program in the computer-readable storage medium described above can be run in an environment deployed in a computer apparatus, such as a client, a host, a proxy device, a server, and the like, and further, in one example, the computer program and any associated data, data files, and data structures are distributed across a networked computer system such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed fashion by one or more processors or computers.
According to an exemplary embodiment of the present disclosure, a computer program product may also be provided, in which instructions are executable by at least one processor to perform the video presentation method as described in the above exemplary embodiment.
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 variations, 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 will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method for video presentation, comprising:
responding to a video playing request of a first target video in videos to be played by a target account, and acquiring the video playing time of the first target video;
determining the playing type of the first target video according to the video playing duration;
updating the prediction result of each second target video belonging to the playing type according to the characteristics of each second target video and the characteristics of a preset number of played videos belonging to the playing type for a plurality of second target videos behind the first target video in the videos to be played;
updating the final prediction result of each second target video according to the updated prediction result of each second target video belonging to the playing type;
and sequencing and displaying the plurality of second target videos according to the updated final prediction result of each second target video.
2. The video presentation method according to claim 1, wherein the step of updating the prediction result of each second target video belonging to the play type according to the characteristics of each second target video and the characteristics of a predetermined number of recently played videos belonging to the play type comprises:
when the playing type is long-distance playing, inputting the characteristics of each second target video and the characteristics of the latest first preset number of played videos belonging to the long-distance playing type into a first prediction model to obtain a long-distance playing score of each second target video, wherein the long-distance playing score represents the possibility that the videos can be long-distance played;
and when the playing type is non-long-play, inputting the characteristics of each second target video and the characteristics of a second preset number of recently played videos belonging to the non-long-play type into a second prediction model to obtain a non-long-play score of each second target video, wherein the non-long-play score represents the possibility that the video cannot be long-played.
3. The video presentation method according to claim 2, wherein the step of inputting the features of each second target video and the features of the latest first predetermined number of the played videos belonging to the long play type into the first prediction model to obtain the long play score of each second target video comprises:
inputting the characteristics of each second target video and the characteristics of the latest first preset number of played videos belonging to the long-play type into a first attention network to obtain the attention result of each played video in the latest first preset number of played videos belonging to the long-play type about the second target video;
taking the attention result of each played video as a weighted value of the characteristics of the played video, and carrying out weighted summation operation on the characteristics of the latest first preset number of played videos belonging to the long play type;
inputting the weighted sum operation result and the characteristics of the second target video into a first splicing layer;
and inputting the vector output by the first splicing layer into the first classification layer after passing through the first full connection layer to obtain the long-play score of the second target video output by the first classification layer.
4. The method of claim 2, wherein the step of inputting the features of each second target video and the features of the latest second predetermined number of played videos belonging to the non-long play type into the second prediction model to obtain the non-long play score of each second target video comprises:
inputting the characteristics of each second target video and the characteristics of the latest second preset number of played videos belonging to the non-long-play type into a second attention network to obtain the attention result of each played video in the latest second preset number of played videos belonging to the non-long-play type, wherein the attention result of each played video is related to the second target video;
taking the attention result of each played video as a weighted value of the characteristics of the played video, and carrying out weighted summation operation on the characteristics of the latest second preset number of played videos belonging to the non-long-play type;
inputting the weighted sum operation result and the characteristics of the second target video into a second splicing layer;
and inputting the vector output by the second splicing layer into the second classification layer after passing through the second full connection layer to obtain the non-long-play score of the second target video output by the second classification layer.
5. The method of claim 2, wherein the final prediction result of each second target video is a difference between the long play score and the non-long play score of the second target video.
6. The video presentation method according to claim 2, further comprising:
receiving the video to be played and the characteristics thereof sent by a server;
and receiving the trained first prediction model and the second prediction model sent by the server.
7. A video presentation apparatus, comprising:
the playing time length obtaining unit is configured to respond to a video playing request of a first target video in videos to be played by a target account, and obtain the video playing time length of the first target video;
a play type determining unit configured to determine a play type of the first target video according to the video play duration;
a play type prediction unit configured to update, for a plurality of second target videos subsequent to the first target video in the video to be played, a prediction result of each second target video belonging to the play type according to a feature of each second target video and a feature of a predetermined number of played videos belonging to the play type at the latest;
a final prediction result obtaining unit configured to update a final prediction result of each second target video according to the updated prediction result of each second target video belonging to the play type;
and the sequencing display unit is configured to sequence and display the plurality of second target videos according to the updated final prediction result of each second target video.
8. An electronic device, comprising:
at least one processor;
at least one memory storing computer-executable instructions,
wherein the computer-executable instructions, when executed by the at least one processor, cause the at least one processor to perform the video presentation method of any one of claims 1 to 6.
9. A computer-readable storage medium, wherein instructions in the computer-readable storage medium, when executed by at least one processor, cause the at least one processor to perform the video presentation method of any one of claims 1 to 6.
10. A computer program product comprising computer instructions, wherein the computer instructions, when executed by at least one processor, implement the video presentation method of any one of claims 1 to 6.
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