Interacting Attention-gated Recurrent Networks for Recommendation
This work addresses the problem of improving recommendation accuracy by better modeling temporal dynamics, which is incremental as it builds on existing attention-based methods with a new interacting scheme.
The paper tackles the problem of capturing temporal dynamics in user preferences for recommendation by addressing the limitations of existing methods that treat all time steps equally and learn user and item dynamics separately. It introduces the Interacting Attention-gated Recurrent Network (IARN), which uses a novel attention scheme to model dependencies between user and item dynamics, resulting in consistent outperformance over state-of-the-art methods on real-world datasets.
Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.