Addressing Cold Start In Recommender Systems: A Semi-Supervised Co-Training Algorithm
SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval Gold Coast Queensland Australia July, 2014(2014)
摘要
Cold start is one of the most challenging problems in recommender systems. In this paper we tackle the cold-start problem by proposing a context-aware semi-supervised co-training method named CSEL. Specifically, we use a factorization model to capture fine-grained user-item context. Then, in order to build a model that is able to boost the recommendation performance by leveraging the context, we propose a semi-supervised ensemble learning algorithm. The algorithm constructs different (weak) prediction models using examples with different contexts and then employs the co-training strategy to allow each (weak) prediction model to learn from the other prediction models. The method has several distinguished advantages over the standard recommendation methods for addressing the cold-start problem. First, it defines a fine-grained context that is more accurate for modeling the user-item preference. Second, the method can naturally support supervised learning and semi-supervised learning, which provides a flexible way to incorporate the unlabeled data.The proposed algorithms are evaluated on two real-world datasets. The experimental results show that with our method the recommendation accuracy is significantly improved compared to the standard algorithms and the cold-start problem is largely alleviated.
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关键词
Cold-start,Recommendation,Semi-supervised Learning
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