Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context  

作　　者：Weihua Liu Haoyang Wan Boyuan Yan 

机构地区：[1]Network Security and Information Management Center,Jining University,Jining,272000,China [2]School of Computer Science,Qufu Normal University,Rizhao,276800,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第4期239-258,共20页工程与科学中的计算机建模（英文）

摘　　要：With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.

关 键 词：Recommendation algorithm user contexts short video temporal contextual information 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]