A deep selective learning network for cross-domain recommendation

In the past two decades, recommendation system has been successfully applied to many e-commerce companies and is a ubiquitous part of today online entertainment. However, many single-domain recommendations suffer from the sparsity problems due to a lack of sufficient interactive data. In fact, user behaviors from different domains are usually relevant. Therefore, cross-domain ideas have been proposed to help alleviate the data sparsity issue in traditional single-domain recommender systems. Motivated by this, we design a deep selective learning network (DSLN) in this paper, for the scenario when domains have minimum or no common users DSLN firstly exploits reviews to profile the preference of users and characteristic of items. Then it selects useful user or item information from the auxiliary domain and transfers it to the target domain to solve the negative transfer problem, even though there may be no overlapping users or items between these two domains. In DSLN model, the selection of useful information is realized by the de-noising auto-encoder (DAE), which is shared between the auxiliary and target domains. By minimizing the reconstruction error of the DAE, on the one hand, only the useful information can be selected from the auxiliary domain; on the other hand, the latent representation of users and items in two domains can be learned. Our experiments on three cross-domain scenarios with different sparsity of Amazon review dataset show that, our proposed model gains 0.58% to 18.16% relative improvement compared to single-domain recommendation models, and from 1.05% to 19.4% relative improvement compared to cross-domain recommendation models. (c) 2022 Elsevier B.V. All rights reserved.