Collaborative Filtering based on Clustering Method using Genre and Interest in SNS

초록

Recommender systems play an important role in online business ecosystem, especially to recommend users’ new items. The most critical problem in the recommender systems is providing high accuracy of recommendation to new users which lack of preference to compute similarity between users. In this paper, we propose a recommender system to solve the cold start problem by combining traditional collaborative filtering of users’ rating preference and the users’ genres interest that derived from SNS. First we compute users’ similarity according to their rating on movies. Second we also compute the users’ similarity from genre interest extracted from SNS. We combine these both similarities information in order to produce new user’s similarity. Our experiment results show that our approach is outperform in cold start problem compared to traditional collaborative filtering.