Attentive-Feature Transfer based on Mapping for Cross-domain Recommendation

Recommendation systems have been widely developed for numerous applications. Existing systems may still suffer from negative transfer or cold starts. These drawbacks are essentially due to overlooking domain-specific users' personal preferences or cross-domain user-item interactions. To address these problems, we propose a cross-domain recommendation algorithm built on a mapping-based attentive feature transfer (MAFT) model. Our MAFT model utilizes matrix factorization and an attention mechanism for fine-grained modeling of user preferences. Then, overlapping cross-domain user features are combined through feature fusion. Moreover, a multilayer perceptron (MLP) is built to map the obtained user features to target-domain user features. Finally, the user-item ratings can be predicted in the target domain. We carried out experiments on the large-scale MovieLens dataset as well as the real Douban Book and Douban Movie datasets. The results show that the precision of the MAFT-based method is clearly higher than those of other cross-domain recommendation methods, especially for cold-start users with few item interactions.