Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks

TL;DR

This paper introduces a novel session-based recommendation model that constructs a session graph to better capture complex item transition patterns, outperforming existing methods on benchmark e-commerce datasets.

Contribution

The paper proposes a new graph neural network approach that considers both sequence order and latent item transition patterns for improved session-based recommendations.

Findings

Outperforms state-of-the-art methods on Yoochoose and Diginetica datasets.

Effectively captures complex item transition patterns.

Demonstrates the benefit of graph-based modeling in session recommendation.

Abstract

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.