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Learning to Recommend Accurate and Diverse Items
2017
Proceedings of the 26th International Conference on World Wide Web - WWW '17
In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of accurate and diverse items as groundtruth for each user. We then represent each user or item as a vector resulted from the factorization of the user-item rating matrix. In our paper, we try to discover a factorization for matching the
doi:10.1145/3038912.3052585
dblp:conf/www/ChengWMSX17
fatcat:7rlsg54qcjdknirmn3gmuu52vi