Sparsity, robustness, and diversification of Recommender Systems

Abstract

Recommender systems have played an important role in helping individuals select useful items or places of interest when they face too many choices. Collaborative filtering is one of the most popular methods used in recommender systems. The idea is to recommend to the target user an item that users with similar tastes will prefer. An important goal of recommender systems is to predict the user's preferences accurately. However, prediction accuracy is not the only evaluation metric in recommender systems. In this dissertation, we will mainly deal with three other aspects of recommender systems, namely sparsity, robustness and diversification.

The dissertation starts with iterative collaborative filtering to overcome sparsity issues in recommender systems. Instead of calculating the similarity matrix using sparse data only once, we iterate this process many times until convergence is achieved. To overcome the sparsity, users' ratings in dense areas are estimated first and these estimates are then used to estimate other ratings in sparse areas. Second, the robustness of recommender system is taken into consideration to detect shilling attacks in recommender systems. Some graph-based algorithms are applied in the user-user similarity graph to detect the highly correlated group, in order to get the group of fake users. Finally, we consider diversification of the types of information used to make recommendations. Specifically, geographical information, temporal information, social network information, and tag information are all aggregated in a biased random walk algorithm to make use of diversified data in multi-dimensional recommender systems.