TLRec:Transfer Learning for Cross-Domain Recommendation

In the era of big data, the available information on the Internet has overwhelmed the human processing capabilities in some commercial applications. Recommendation techniques are indispensable to predict user ratings on items in terms of historical data and deal with the information overload. In many applications, the problem of data sparsity usually results in overfitting and fails to give desirable performance. Therefore, many works have started to investigate the techniques of cross-domain recommendation to overcome the challenge. However, it is not trivial. In this paper, we propose a transfer learning algorithm, named TLRec, for cross-domain recommendation, which exploits the overlapped users and items as a bridge to link different domains and implements knowledge transfer. We learn parameters based on the defined empirical prediction error, smoothness and regularization of user and item latent vectors. We also establish a relation between TLRec and vertex vectoring on bipartite graphs. The experimental result illustrates that TLRec has promising performance and outperforms several state-of-the art approaches on a real dataset.