Neural Attention Networks for Recommendation With Auxiliary Data

With the rapid development of Internet technologies, an increasing amount of auxiliary data can be readily obtained through Web services. To alleviate the data sparsity issue, auxiliary data based recommendation has emerged for better recommendation performance. However, existing auxiliary data based methods suffer from two problems. First, only the relation features related to the meta-paths are extracted from auxiliary data, which may lead to features useful for recommendation being lost irreversibly. Second, an assumption is made that an individual has the same preference over the identical characteristic of different items, which is often invalid and may lead to misleading recommendations. Actually, a user may place different importance on the same feature of different items, and an item may get different attention from the same feature of different users. In this paper, we propose a neural network framework, named Neural Attention Recommendation model (NARec), for auxiliary data based collaborative filtering. For the first problem, we characterize users and items from three aspects, namely latent features, attribute features, and meta-path based relation features, which can comprehensively extract the useful recommendation features from auxiliary data. Regarding the second problem, we integrate different user features and item features into an attention mechanism based rating prediction model for recommendation, which can adaptively characterize the personalized features of users and items. Extensive experiments on three real-world datasets demonstrate that NARec significantly outperforms the state-of-the-art recommendation methods in the rating prediction task.