A video recommendation algorithm based on the combination of video content and social network. (23rd June 2016)

• Record Type: Journal Article
• Authors: Cui, Laizhong; Dong, Linyong; Fu, Xianghua; Wen, Zhenkun; Lu, Nan; Zhang, Guanjing
• Other Names: Hassan Houcine guestEditor.; Yang Laurence T. guestEditor.; Qiu Meikang guestEditor.
• Is Part Of: Concurrency and computation. Volume 29:Number 14(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-06-23
• Subjects: recommendation -- online video -- social network -- similarity; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3900
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Recently, social network has been one of the biggest information exchange platforms of the Internet. Moreover, the users in social network used to watch videos through social network application. To provide a proper recommended video list, the video recommendation algorithm for social network is becoming a hot research issue. On one hand, more and more researchers introduce the concept of trust into video recommendation algorithms. However, most of them only select the trust friends based on the similarity and neglect the characteristics of social network. On the other hand, most previous video recommendation algorithms are only based on the number that a video is viewed to evaluate a video's quality. They do not make good use of the social relationship in social network and the video's reputation. This paper mainly focuses on the challenge that the effectiveness and performance of current video recommendation algorithm in social network cannot satisfy the users. In this paper, we propose a novel video recommendation algorithm based on the combination of video content and social network. Our proposed algorithm consists of the trust friends computing model and video's quality evaluation model. The trust friends computing method takes into account similarity between users, interaction between users, and the active degree of a user. In our video's quality evaluation model, we combine the acceptance ratio of a video with a video's reputation. The video can be given an appropriate rating score through this model. We design corresponding trust friends computing algorithm and video recommendation algorithm respectively for two proposed models. Our integral video recommendation algorithm consists of these two algorithms. The experimental results indicate that the performance and effectiveness of our algorithm are better than those of two classical video recommendation algorithms (i.e., user‐based collaborative filtering algorithm and TBR‐d algorithm), in terms of precision, recall and F1‐measure . Copyright © 2016 John Wiley & Sons, Ltd.