1. Tensor-Based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations
- Author
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Evgeny Frolov and Ivan Oseledets
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Artificial Intelligence (cs.AI) ,General Computer Science ,Computer Science - Artificial Intelligence ,Statistics - Machine Learning ,General Engineering ,Machine Learning (stat.ML) ,General Materials Science ,Electrical and Electronic Engineering ,Information Retrieval (cs.IR) ,Machine Learning (cs.LG) ,Computer Science - Information Retrieval - 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 and compares competitively to its neural counterpart., 15 pages, 6 figures, submitted to IEEE Access
- Published
- 2023