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Collaborative filtering (CF), one of the most successful recommendation approaches, continues to attract interest in both academia and industry. However, one key issue limiting the success of collaborative filtering in certain application domains is the cold-start problem, a situation where historical data is too sparse (known as the sparsity problem), new users have not rated enough items (known as the new user problem), or both. In this paper, we aim at addressing the cold-start problem bydoi:10.1145/2043932.2043969 dblp:conf/recsys/HuP11 fatcat:keeuihdyqbdfliyyeqqqiwrqhq