Attention-Based Neural Tag Recommendation

Personalized tag recommender systems suggest tags to users when annotating specific items. Usually, recommender systems need to take both users' preference and items' features into account. Existing methods like latent factor models based on tensor factorization use low-dimensional dense vectors to represent latent features of users, items and tags. The problem with these models is using the static representation for the user, which neglects that users' preference keeps evolving over time. Other methods based on base-level learning (BLL) only use a simple time-decay function to weight users' preference. In this paper, we propose a personalized tag recommender system based on neural networks and attention mechanism. This approach utilizes the multi-layer perceptron to model the non-linearities of interactions among users, items and tags. Also, an attention network is introduced to capture the complex pattern of the user's tagging sequence. Extensive experiments on two real-world datasets show that the proposed model outperforms the state-of-the-art tag recommendation method.