关键词: 自注意力机制, 过滤算法, 时间卷积网络, 序列推荐, 对比学习

Abstract: Aiming at the problems of noise arising from user's unexpected interactions in practical recommendation scenarios and the challenge of capturing short-term demand biases due to the dispersed attention in self-attention mechanism, a model namely FTARec (sequential Recommendation based on hierarchical Filter and Temporal convolution enhanced self-Attention network) was proposed. Firstly, hierarchical filter was used to filter noise in the original data. Then, user embeddings were obtained by combining temporal convolution enhanced self-attention networks with decoupled hybrid location encoding. The deficiencies in modeling short-term dependencies among items were supplemented by enhancing the self-attention network with temporal convolution in this process. Finally, contrastive learning was incorporated to refine user embeddings and predictions were made based on the final user embeddings. Compared to existing sequential recommendation models such as the Self-Attentive Sequential Recommendation (SASRec) and the Filter-Enhanced Multi-Layer Perceptron approach for sequential Recommendation (FMLP-Rec), FTARec achieves higher Hit Rate (HR) and Normalized Discounted Cumulative Gain (NDCG) on three publicly available datasets: Beauty, Clothing, and Sports. Compared with the suboptimal DuoRec, FTARec has the HR@10 index increased by 7.91%, 13.27%, 12.84%, and the NDCG@10 index increased by 5.52%, 8.33%, 9.88%, respectively, verifying the effectiveness of the proposed model.

Key words: self-attention mechanism, filtering algorithm, temporal convolutional network, sequential recommendation, contrastive learning