Improving Social Recommendations by applying a Personalized Item Clustering Policy

In online Recommender Systems, people tend to consume and rate items that are not necessarily similar to one another. This phenomenon is a direct consequence of the fact that human taste is influenced by many factors that cannot be captured by pure Content-based or Collaborative Filtering approaches. For this reason, a desirable property of Recommender Systems would be to identify correlations between seemingly different items that might be of interest to a particular user. This course of action is expected to improve the novelty and the diversity of the recommendations and therefore increase user satisfaction. In this paper, we address this problem by proposing a socially-aware personalized item clustering recommendation algorithm. We are trying to locate patterns between the items that a user has evaluated by grouping them into different clusters according to the rating behavior of the members of his Personal Network, which includes the individuals in his direct social network and those other persons that the user exhibits a similar item evaluation behavior. Once the clustering phase has been completed, we use each cluster’s members as seed items in order to construct an item consumption network. Then, by performing a random walk on the aforementioned network, we are able to produce recommendations that are accurate and at the same time novel and diverse. Preliminary results reveal the potential of this idea.