An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention

arXiv:2312.10325v2 Announce Type: replace
Abstract: Sequential recommendation (SR) models based on Transformers have achieved remarkable successes. The self-attention mechanism of Transformers for computer vision and natural language processing suffers from the oversmoothing problem, i.e., hidden representations becoming similar to tokens. In the SR domain, we, for the first time, show that the same problem occurs. We present pioneering investigations that reveal the low-pass filtering nature of self-attention in the SR, which causes oversmoothing. To this end, we propose a novel …

arxiv attention beyond bias cs.ai cs.ir cs.lg inductive recommendation self-attention type