An Approach for Building Efficient and Accurate Social Recommender Systems using Individual Relationship Networks - 2017


Social recommender system, using social relation networks as further input to enhance the accuracy of ancient recommender systems, has become an necessary analysis topic. But, most existing strategies utilize the complete user relationship network with no thought to its huge size, sparsity, imbalance, and noise issues. This may degrade the efficiency and accuracy of social recommender systems. This study proposes a new approach to manage the complexity of adding social relation networks to recommender systems. Our method first generates a private relationship network (IRN) for each user and item by developing a unique fitting algorithm of relationship networks to control the connection propagation and contracting. We then fuse matrix factorization with social regularization and also the neighborhood model using IRN's to generate recommendations. Our approach is sort of general, and can additionally be applied to the item-item relationship network by switching the roles of users and items. Experiments on four datasets with different sizes, sparsity levels, and relationship types show that our approach can improve predictive accuracy and gain a better scalability compared with state-of-the-art social recommendation methods.

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