A Semi-supervised Solution for Cold Start Issue on Recommender Systems

Zhifeng Hao, Yingchao Cheng, Ruichu Cai, Wen Wen, Lijuan Wang

Abstract: The recommender system is the most competitive solution to solve information overload problem, and has been extensively applied. The current collaborative filtering based recommender systems explore users’ latent interest with their historical online behavior records. They are all facing the cold start issue. In this work, we proposed a background-based semi-supervised tri-training method named BSTM to tackle this problem. In detail, we capture fine-grained users’ background information by using a factorization model. By exploring these information, the performance of our recommendation can be significantly promoted. Besides, we proposed a semi-supervised ensemble algorithm, which got both labeled and unlabeled data involved. This algorithm assembled diverse weak prediction models which are generated by exploring samples with diverse background information and by tri-training tactic. The experimental results show that, with this solution, the accuracy of recommendation is significantly improved, and the cold-start issue is alleviated.