AAAI Publications, Twenty-Fourth International Joint Conference on Artificial Intelligence

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Ice-Breaking: Mitigating Cold-Start Recommendation Problem by Rating Comparison
Jingwei Xu, Yuan Yao, Hanghang Tong, Xianping Tao, Jian Lu

Last modified: 2015-06-27


Recommender system has become an indispensable component in many e-commerce sites. One major challenge that largely remains open is the cold-start problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RaPare) to break this ice barrier. The center-piece of  our RaPare is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and warm users/items. We instantiate our RaPare strategy on the prevalent method in recommender system, i.e., the matrix factorization based collaborative filtering. Experimental evaluations on two real data sets validate the superiority of our approach over the existing methods in cold-start scenarios.

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