Community-based recommender systems have attracted much research attention. Forming communities allows us to reduce data sparsity and focus on discovering the latent characteristics of communities instead of individuals. Previous work focused on how to detect the community using various algorithms. However, they failed to consider users' social attributes, such as social activeness and dynamic interest, which have strong correlations to users' preference and choice. Intuitively, people have different social activeness in a social network. Ratings from users with high activeness are more likely to be trustworthy. Temporal dynamic of interest is also significant to user's preference. In this paper, we propose a novel community-based framework. We first employ PLSA-based model incorporating social activeness and dynamic interest to discover communities. Then the state-of-the-art matrix factorization method is applied on each of the communities. The experiment results on two real world datasets validate the effectiveness of our method for improving recommendation performance.
['Bin Yin', 'Yujiu Yang', 'Wenhuang Liu']
Exploring social activeness and dynamic interest in community-based recommender system