Neural Session-Aware Recommendation

Recommender systems help users find items they are likely to interact within the near future, such as products to buy in e-commerce or songs to play in music websites. The Traditional recommendation methods make predictions based on long-term user profiles, i.e., the items a user interacted with in the past while ignoring the time and order of the interactions. Recent findings, however, suggest that users may exhibit interest to items in a certain order depending on situations and more recent items in a sequence have a larger impact on the subsequent choices. Moreover, in many practical applications, user-item interactions are organized into short sessions, where each session reflects the user's short-term interest in addition to long-term preferences. Leveraging both long-term user profiles and short-term sequential patterns from sessions can lead to more accurate models known as the session-aware recommendation methods. In this paper, we explore various strategies to integrate user long-term preferences with session patterns encoded by recurrent neural networks (RNNs). The strategies include integrating user embeddings with input and output of session RNNs, integrating with fixed or adaptive contributions of the user and session components by using a specially designed gating mechanism. We conducted an empirical evaluation of three publicly available datasets. The results indicate that combining user long-term profiles with the output of session RNNs yields improved predictions and the proposed adaptive integration model outperforms the state-of-the-art sequential and session-aware recommendation methods.