| dc.description.abstract | With the popularity of e-commerce and mobile phones, recommender system becomes a necessary tool to help users find their desired commodities. However, recommendation systems suffer two critical issues, sparsity problem and cold start problem. Several studies use cross domain recommendation to address the data sparsity problems, which transfer the information from the richer domain to the sparser domain. In this paper, by combining the MF and recurrent neural network (RNN), we develop a new framework for cross-domain recommender system namely CD-ELR. At the first step, we aim to learn the latent factors of users and items by latent factor model, MF. Then, we choose two combination operators, i.e., Max-pooling, and Average-pooling, to combine the latent factors of common users. After obtain the latent factors of common users in target and source domain, we capture the evolutions of latent factors by recurrent neural network (RNN) to make prediction of user preference in the future. The experimental results show that CD-ELR has better performance than other state-of-the-art recommendation systems. In addition, we carry out the experiments on several datasets to prove the practicability of proposed CD-ELR. | en_US |