A Memory-Based Collaborative Filtering Recommender System Using Social Ties

Recent studies have illustrated that social networks are valuable sources of information which can be used for various purposes. In recommender systems, researchers have been motivated to utilize social information not only to enhance the prediction accuracy, but also to address the data sparseness challenges such as cold start problem. In this paper, we propose a social memory-based Collaborative Filtering (CF) approach and examine the impact of integrating social ties on both accuracy and prediction coverage. A simple learning method is used to tune the contribution of social information, in the proposed similarity measure, as a weight parameter. We compared our experimental results with several state-of-the-art memory-based and model-based CF approaches using Epinions dataset to investigate the performance of our method in extreme sparsity of real-world data. Results show that our approach provide high accuracy as well as reasonable prediction coverage comparing with other methods.