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.

本研究提出了基于张量分解的模型来嵌入一些关于顺序数据的结构性知识，从而在解决下一个物品推荐任务时比深度神经网络更加轻便高效

基于 Hankel 矩阵表示的张量串行学习用于下一物品推荐

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

Predicting a user's preference in a short anonymous interaction session
instead of long-term history is a challenging problem in the real-life
session-based recommendation, e.g., e-commerce and media stream. Recent
research of the session-based recommender system mainly focuses on sequential
patterns by utilizing the attention mechanism, which is straightforward for the
session's natural sequence sorted by time. However, the user's preference is
much more complicated than a solely consecutive time pattern in the transition
of item choices. In this paper, therefore, we study the item transition pattern
by constructing a session graph and propose a novel model which collaboratively
considers the sequence order and the latent order in the session graph for a
session-based recommender system. We formulate the next item recommendation
within the session as a graph classification problem. Specifically, we propose
a weighted attention graph layer and a Readout function to learn embeddings of
items and sessions for the next item recommendation. Extensive experiments have
been conducted on two benchmark E-commerce datasets, Yoochoose and Diginetica,
and the experimental results show that our model outperforms other
state-of-the-art methods.

本文提出了一种新的基于会话图的推荐模型，该模型通过构建会话图，并协同考虑序列顺序和会话图的潜在顺序，将下一个项目推荐问题建立为图分类问题，并利用加权注意图层和读取函数对项目和会话进行嵌入学习。该模型在两个基准电子商务数据集，Yoochoose 和 Diginetica 上表现出比其他最先进方法更好的性能。