A Self-Attention Mask Learning-Based Recommendation System

The primary purpose of sequence modeling is to record long-term interdependence across interaction sequences, and since the number of items purchased by users gradually increases over time, this brings challenges to sequence modeling to a certain extent. Relationships between terms are often overlooked, and it is crucial to build sequential models that effectively capture long-term dependencies. Existing methods focus on extracting global sequential information, while ignoring deep representations from subsequences. We argue that limited item transfer is fundamental to sequence modeling, and that partial substructures of sequences can help models learn more efficient long-term dependencies compared to entire sequences. This paper proposes a sequence recommendation model named GAT4Rec (Gated Recurrent Unit And Transformer For Recommendation), which uses a Transformer layer that shares parameters across layers to model the user's historical interaction sequence. The representation learned by the gated recurrent unit is used as a gating signal to filter out better substructures of the user sequence. The experimental results demonstrate that our proposed GAT4Rec model is superior to other models and has a higher recommendation effectiveness.