SPGL: Enhancing Session-based Recommendation with Single Positive Graph Learning

TL;DR

SPGL introduces a graph-based session recommendation model that leverages single positive optimization loss and global item representations to address data sparsity and improve recommendation accuracy.

Contribution

The paper proposes SPGL, a novel session-based recommendation approach that uses graph convolutional networks and single positive loss to enhance data efficiency and transferability.

Findings

Outperforms existing methods on Tmall, RetailRocket, and Diginetica datasets.

Effectively captures item relationships and session dynamics.

Improves recommendation accuracy with less complex models.

Abstract

Session-based recommendation seeks to forecast the next item a user will be interested in, based on their interaction sequences. Due to limited interaction data, session-based recommendation faces the challenge of limited data availability. Traditional methods enhance feature learning by constructing complex models to generate positive and negative samples. This paper proposes a session-based recommendation model using Single Positive optimization loss and Graph Learning (SPGL) to deal with the problem of data sparsity, high model complexity and weak transferability. SPGL utilizes graph convolutional networks to generate global item representations and batch session representations, effectively capturing intrinsic relationships between items. The use of single positive optimization loss improves uniformity of item representations, thereby enhancing recommendation accuracy. In the intent extractor, SPGL considers the hop count of the adjacency matrix when constructing the directed global graph to fully integrate spatial information. It also takes into account the reverse positional information of items when constructing session representations to incorporate temporal information. Comparative experiments across three benchmark datasets, Tmall, RetailRocket and Diginetica, demonstrate the model's effectiveness. The source code can be accessed on https://github.com/liang-tian-tian/SPGL .