EP-4738814-A1 - VIDEO EVALUATION METHOD AND RELATED DEVICES THEREOF

EP4738814A1EP 4738814 A1EP4738814 A1EP 4738814A1EP-4738814-A1

Abstract

This application discloses a video assessment method and a related device thereof, to accurately assess a plurality of videos, thereby accurately obtaining degrees of interest to a user for the plurality of videos. The method in this application includes: obtaining a first video group, where the first video group includes a plurality of first videos (401); processing watch times of the plurality of first videos to obtain parameters of the first video group, where the parameters of the first video group indicate biases of the watch times of the plurality of first videos and noise of the watch times of the plurality of first videos (402); and obtaining assessment values of the plurality of first videos based on the parameters of the first video group and the watch times of the plurality of first videos, where the assessment values of the plurality of first videos indicate degrees of interest to a user for the plurality of first videos (403).

Inventors

CAI, Guohao
ZHAO, Haiyuan
DONG, Zhenhua
XU, JUN

Assignees

Huawei Technologies Co., Ltd.
Renmin University of China

Dates

Publication Date: 20260506
Application Date: 20240715

Claims (19)

A video assessment method, wherein the method comprises: obtaining a first video group, wherein the first video group comprises a plurality of first videos; processing watch times of the plurality of first videos, to obtain parameters of the first video group, wherein the parameters of the first video group indicate biases of the watch times of the plurality of first videos and noise of the watch times of the plurality of first videos; and obtaining assessment values of the plurality of first videos based on the parameters of the first video group and the watch times of the plurality of first videos, wherein the assessment values of the plurality of first videos indicate degrees of interest to a user for the plurality of first videos.
The method according to claim 1, wherein processing the watch times of the plurality of first videos, to obtain the parameters of the first video group comprises: processing the watch times of the plurality of first videos by using a Gaussian mixture model, to obtain the parameters of the first video group.
The method according to claim 1 or 2, wherein obtaining the assessment values of the plurality of first videos based on the parameters of the first video group and the watch times of the plurality of first videos comprises: performing a moving average operation on the parameters of the first video group and parameters of the second video group, to obtain new parameters of the first video group, wherein the new parameters of the first video group indicate new biases of the watch times of the plurality of first videos and new noise of the watch times of the plurality of first videos; and obtaining the assessment values of the plurality of first videos based on the new parameters of the first video group and the watch times of the plurality of first videos.
The method according to claim 3, wherein obtaining the assessment values of the plurality of first videos based on the new parameters of the first video group and the watch times of the plurality of first videos comprises: performing a first affine transformation operation on a watch time of a target video and the new parameters of the first video group, to obtain an assessment value of the target video, wherein the target video is any one of the plurality of first videos.
The method according to claim 3, wherein obtaining the assessment values of the plurality of first videos based on the new parameters of the first video group and the watch times of the plurality of first videos comprises: performing a second affine transformation operation on a watch time of a target video, a preset hyperparameter, and the new parameters of the first video group, to obtain an assessment value of the target video, wherein the target video is any one of the plurality of first videos.
The method according to claim 4, wherein the first affine transformation operation comprises at least one of the following: a subtraction operation and a division operation.
The method according to claim 5, wherein the second affine transformation operation comprises at least one of the following: an exponential operation, a subtraction operation, a multiplication operation, and a division operation.
The method according to any one of claims 3 to 7, wherein the second video group comprises a plurality of second videos, durations of the plurality of first videos are within a preset first range, durations of the plurality of second videos are within a preset second range, and the first range and the second range do not overlap each other.
A video assessment apparatus, wherein the apparatus comprises: a first obtaining module, configured to obtain a first video group, wherein the first video group comprises a plurality of first videos; a processing module, configured to process watch times of the plurality of first videos, to obtain parameters of the first video group, wherein the parameters of the first video group indicate biases of the watch times of the plurality of first videos and noise of the watch times of the plurality of first videos; and a second obtaining module, configured to obtain assessment values of the plurality of first videos based on the parameters of the first video group and the watch times of the plurality of first videos, wherein the assessment values of the plurality of first videos indicate degrees of interest to a user for the plurality of first videos.
The apparatus according to claim 9, wherein the processing module is configured to process the watch times of the plurality of first videos by using a Gaussian mixture model, to obtain the parameters of the first video group.
The apparatus according to claim 9 or 10, wherein the second obtaining module is configured to: perform a moving average operation on the parameters of the first video group and parameters of the second video group, to obtain new parameters of the first video group, wherein the new parameters of the first video group indicate new biases of the watch times of the plurality of first videos and new noise of the watch times of the plurality of first videos; and obtain the assessment values of the plurality of first videos based on the new parameters of the first video group and the watch times of the plurality of first videos.
The apparatus according to claim 11, wherein the second obtaining module is configured to perform a first affine transformation operation on a watch time of a target video and the new parameters of the first video group, to obtain an assessment value of the target video, wherein the target video is any one of the plurality of first videos.
The apparatus according to claim 11, wherein the second obtaining module is configured to perform a second affine transformation operation on a watch time of a target video, a preset hyperparameter, and the new parameters of the first video group, to obtain an assessment value of the target video, wherein the target video is any one of the plurality of first videos.
The apparatus according to claim 12, wherein the first affine transformation operation comprises at least one of the following: a subtraction operation and a division operation.
The apparatus according to claim 13, wherein the second affine transformation operation comprises at least one of the following: an exponential operation, a subtraction operation, a multiplication operation, and a division operation.
The apparatus according to any one of claims 11 to 15, wherein the second video group comprises a plurality of second videos, durations of the plurality of first videos are within a preset first range, durations of the plurality of second videos are within a preset second range, and the first range and the second range do not overlap each other.
A video assessment apparatus, wherein the video assessment apparatus comprises a memory and a processor, the memory stores code, the processor is configured to execute the code, and when the code is executed, the video assessment apparatus performs the method according to any one of claims 1 to 8.
A computer storage medium, wherein the computer storage medium stores one or more instructions, and when the instructions are executed by one or more computers, the one or more computers are enabled to perform the method according to any one of claims 1 to 8.
A computer program product, wherein the computer program product stores instructions, and when the instructions are executed by a computer, the computer is enabled to perform the method according to any one of claims 1 to 8.

Description

This application claims priority to Chinese Patent Application No. 202310885569.X, filed with the China National Intellectual Property Administration on July 18, 2023 and entitled "VIDEO ASSESSMENT METHOD AND RELATED DEVICE THEREOF", which is incorporated herein by reference in its entirety. TECHNICAL FIELD Embodiments of this application relate to artificial intelligence (artificial intelligence, AI) technologies, and in particular, to a video assessment method and a related device thereof. BACKGROUND With the emergence of video content platforms, more users are exposed to and watch various videos in their daily life. Accurate video recommendation plays an important role in meeting user requirements and participation. Therefore, a neural network model used to complete video recommendation emerges. In a model training process, the model learns degrees of interest to a user for a plurality of videos, so that the model can more accurately complete video recommendation. A watch time of a video is one of important indicators for measuring a degree of interest to the user for the video. In a related technology, watch times of the plurality of videos may be calculated, to obtain a mean value of the watch times of the plurality of videos and a variance of the watch times of the plurality of videos. Then, further calculation is performed on the watch times of the plurality of videos, the mean value of the watch times of the plurality of videos, and the variance of the watch times of the plurality of videos, to obtain assessment values of the plurality of videos. The assessment values of the plurality of videos indicate the degrees of interest to the user for the plurality of videos. In the foregoing process, when the assessment values of the plurality of videos are obtained based on the watch times of the plurality of videos, it is assumed that the degrees of interest to the user for the plurality of videos comply with Gaussian distribution. Factors considered in this setting are relatively single and do not conform to an actual case. Consequently, the obtained assessment values of the plurality of videos are not accurate enough. In other words, the degrees of interest to the user for the plurality of videos cannot be accurately obtained. SUMMARY Embodiments of this application provide a video assessment method and a related device thereof, to accurately assess a plurality of videos, thereby accurately obtaining degrees of interest to a user for the plurality of videos. According to a first aspect, an embodiment of this application provides a video assessment method. The method includes: When video assessment needs to be performed, a batch of videos may be obtained first, and the batch of videos are divided into a plurality of video groups according to a specific standard. For one of the plurality of video groups, that is, a first video group, the first video group may include a plurality of first videos. After the first video group is obtained, watch times of a plurality of first videos may be processed, to obtain parameters of the first video group. The parameters of the first video group include duration bias items of the first video group and noise watching items of the first video group. The duration bias items of the first video group indicate biases of the watch times of the plurality of first videos. The noise watching items of the first video group indicate noise of the watch times of the plurality of first videos. After the parameters of the first video group are obtained, the parameters of the first video group and the watch times of the plurality of first videos may be further processed, to obtain assessment values of the plurality of first videos. Actual assessment values of the plurality of first videos indicate degrees of interest to a user for the plurality of first videos. This is equivalent to obtaining the degrees of interest to the user for the plurality of first videos. A similar operation may also be performed on another video group in the plurality of video groups except the first video group. Therefore, assessment values of all videos in the batch of videos, that is, degrees of interest to the user for all the videos in the batch of videos, may be obtained. In this way, video assessment is completed. It can be learned from the foregoing method that when the plurality of video groups need to be assessed, the first video group including the plurality of first videos may be first obtained from the plurality of video groups. Then, the watch times of the plurality of first videos may be processed, to obtain the parameters of the first video group. Next, the parameters of the first video group and the watch times of the plurality of first videos may be further processed, to obtain the assessment values of the plurality of first videos. This is equivalent to obtaining the degrees of interest to the user for the plurality of first videos. An operation similar to the operation performed on the first