Exploring A Trust Based Recommendation Approach for Videos in Online Social Network

With the rapid development of social network, more and more users watch videos through social network, such as Sina Weibo. Traditional video recommendation algorithms aim at online video systems and they neglect the social relationship and propagation features in social network. The interaction information among users in social network could help improve the effect of video recommendation in social network. This paper mainly focuses on the problem that current video recommendation methods for videos in online social network can not meet the needs of the users. To address this challenge, we propose a new trust based video recommendation approach including a user discovery model and a video discovery model in this paper. To discover influential users of the target user, we divide the other users into direct influential users and indirect influential users. We compute the trust between the target user and each of his/her influential users based on user similarity, friendship and interaction. In the video discovery model, we calculate the video trust based on the video rating and video activity. Through combing the user discovery model and video discovery model, we present our trust based recommendation algorithm for videos in social network. The experimental results demonstrate that our approach can outperform two classical video recommendation algorithms, in terms of precision, recall and F1-measure.