Unsupervised Graph Embeddings for Session-based Recommendation with Item Features

TL;DR

This paper introduces GCNext, an unsupervised graph convolutional approach that integrates item features into session-based recommendation models, significantly improving their accuracy across multiple datasets.

Contribution

It presents a novel method combining graph neural networks and item features for session-based recommendations, enhancing existing models' performance.

Findings

GCNext improves MRR@20 by up to 12.79%

Integrating item features boosts recommendation accuracy

The method is easy to incorporate into existing systems

Abstract

In session-based recommender systems, predictions are based on the user's preceding behavior in the session. State-of-the-art sequential recommendation algorithms either use graph neural networks to model sessions in a graph or leverage the similarity of sessions by exploiting item features. In this paper, we combine these two approaches and propose a novel method, Graph Convolutional Network Extension (GCNext), which incorporates item features directly into the graph representation via graph convolutional networks. GCNext creates a feature-rich item co-occurrence graph and learns the corresponding item embeddings in an unsupervised manner. We show on three datasets that integrating GCNext into sequential recommendation algorithms significantly boosts the performance of nearest-neighbor methods as well as neural network models. Our flexible extension is easy to incorporate in state-of-the-art methods and increases the MRR@20 by up to 12.79%.