Local-Aware Hierarchical Attention for Sequential Recommendation

Abstract

Modeling the dynamic preferences of users is a challenging and essential task in a recommendation system. Taking inspiration from the successful use of self-attention mechanisms in tasks within natural language processing, several approaches have initially explored integrating self-attention into sequential recommendation, demonstrating promising results. However, existing methods have overlooked the intrinsic structure of sequences, failed to simultaneously consider the local fluctuation and global stability of users’ interests, and lacked user information. To address these limitations, we propose LHASRec (Local-Aware Hierarchical Attention for Sequential Recommendation), a model that divides a user’s historical interaction sequences into multiple sessions based on a certain time interval and computes the weight values for each session. Subsequently, the calculated weight values are combined with the user’s historical interaction sequences to obtain a weighted user interaction sequence. This approach can effectively reflect the local fluctuation of the user’s interest, capture the user’s particular preference, and at the same time, consider the user’s general preference to achieve global stability. Additionally, we employ Stochastic Shared Embeddings (SSE) as a regularization technique to mitigate the overfitting issue resulting from the incorporation of user information. We conduct extensive experiments, showing that our method outperforms other competitive baselines on sparse and dense datasets and different evaluation metrics.