Tensor-Based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations

Abstract

Self-attentive transformer models have recently been shown to solve the next item recommendation task very efficiently. The learned attention weights capture sequential dynamics in user behavior and generalize well. Motivated by the special structure of learned parameter space, we question if it is possible to mimic it with an alternative and more lightweight approach. We develop a new tensor factorization-based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation. The resulting model has a shallow linear architecture. Remarkably, it achieves significant speedups in training time over its neural counterpart and performs competitively in terms of the quality of recommendations.