Multi-scale filter-enhanced transformer for sequential recommendation
Xiyu Cui · Applied and Computational Engineering · 2023
Recent research has shown that self-attention mechanisms improve Sequential Recommender Systems (SRS) by capturing sequential associations with the interactions. Nevertheless, existing work still needs to address two critical limitations. Firstly, the behavior of users in the original sequences contains various preference signals that are implicit and noisy and hard to reflect the user’s intentions fully. As a result, it would deteriorate the representation of their true intentions to model all interactions. Secondly, most models only model single-scale interaction sequences and ignore the multi-scale feature relationships of the sequences. In order to address these limitations, the paper proposes MFTSRec (Multi-scale Filter Enhanced Transformer Sequential Recommender), which can weaken those interactions irrelevant to the users’ intentions from their implicit feedback and adaptively focus on the user’s multi-scale intentions. Besides, this paper also does extensive experiments on four benchmark datasets and further demonstrates the effectiveness and robustness of MFTSRec compared to the state-of-the-art model.