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Improving Recommender Systems by Incorporating Social Contextual Information
2011
ACM Transactions on Information Systems
Due to their potential commercial value and the associated great research challenges, recommender systems have been extensively studied by both academia and industry recently. However, the data sparsity problem of the involved user-item matrix seriously affects the recommendation quality. Many existing approaches to recommender systems cannot easily deal with users who have made very few ratings. In view of the exponential growth of information generated by online users, social contextual
doi:10.1145/1961209.1961212
fatcat:wgh23g4b4zasboklcml76o3pyi