Jianxun Lian
Xing Xie
ABSTRACT
To overcome data sparsity problem, we propose a cross domain recommendation system named CCCFNet which can combine collaborative filtering and content-based filtering in a unified framework. We first introduce a factorization framework to tie CF and content-based filtering together. Then we find that the MAP estimation of this framework can be embedded into a multi-view neural network. Through this neural network embedding the framework can be further extended by advanced deep learning techniques.
- A. M. Elkahky, Y. Song, and X. He. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th International Conference on World Wide Web, WWW '15, pages 278--288, New York, NY, USA, 2015. ACM. Google Scholar
Digital Library
- W. Pan, N. N. Liu, E. W. Xiang, and Q. Yang. Transfer learning to predict missing ratings via heterogeneous user feedbacks. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume Three, IJCAI'11, pages 2318--2323. AAAI Press, 2011. Google ScholarDigital Library
- A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '08, pages 650--658, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
Index Terms
- CCCFNet: A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems
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