CCCFNet: A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems

Jianxun Lian, Fuzheng Zhang, Xing Xie, Guangzhong Sun

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.