Network education video recommendation algorithm based on context and trust relationship
Chenyang Zhao, Junling Wang · 2013
With the development of information technology and the internet, there are many education videos on the network for people to study in their spare time. However, it is difficult for people to choose education videos which they really need. To solve the problem, a personalized recommendation algorithm based on context and trust relationship is proposed in this paper. Under the help of this algorithm, education videos interested by users can be proactive recommended. The algorithm improves traditional filtering recommendation algorithm. It is divided into three parts. One candidate video set is firstly obtained according to user-rating matrix and context, and then another set is obtained according to trust relationships between users. Finally, the former two candidate sets are combined to determine the recommendation video set. Experiments indicate that the proposed algorithm is more accurate than traditional collaborative filtering algorithm.