基于云计算的短视频媒体资源个性化推送方法

Personalized Push Method of Short Video Media Resources Based on Cloud Computing

为提高短视频媒体资源推送的匹配度,提出基于云计算的短视频媒体资源个性化推送方法。设计基于Hadoop的短视频媒体资源个性化推送框架,以采集的信息为基础,采用隐含狄利克雷分布(latent Dirichlet allocation,LDA)模型按主题分类短视频类型,并通过基于注意力机制的卷积神经网络模型识别精彩瞬间短视频主题;短视频媒体资源推荐模块根据用户的历史短视频浏览行为,确定用户对未浏览短视频的兴趣值,根据其值大小生成推荐列表,通过数据展示层将推送结果呈现给用户。实验结果表明:该方法可实现用户感兴趣主题短视频媒体资源的个性化推送,当各弹幕文本提取的最佳主题数量为2、推荐列表长度为3时,推送效果最突出;该方法能提高短视频媒体资源个性化推送的性能,推送的内容更加符合用户的兴趣度。

In order to improve the matching degree of short video media resource push,a personalized short video media resource push method based on cloud computing is proposed.A Hadoop-based personalized push framework for short video media resources is designed,based on the collected information,the latent Dirichlet allocation model is used to classify the types of short videos according to the theme,and the convolutional neural network model based on the attention mechanism is used to identify the theme of short videos at wonderful moments;The short video media resource recommendation module determines the interest value of the user in the unbrowsed short video according to the user’s historical short video browsing behavior,generates a recommendation list according to the value,and presents the push result to the user through the data presentation layer.The experimental results show that the method can realize the personalized push of short video media resources with the topics that users are interested in,and when the optimal number of topics extracted from each bullet comment text is 2 and the length of the recommendation list is 3,the push effect is the most prominent;the method can improve the performance of personalized push of short video media resources,and the pushed content is more in line with the user’s interest.