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arXiv · 2502.13763

Unsupervised Graph Embeddings for Session-based Recommendation with Item Features

Andreas Peintner·Marta Moscati·Emilia Parada-Cabaleiro·Markus Schedl·Eva Zangerle
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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%.

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Andreas Peintner, Marta Moscati, Emilia Parada-Cabaleiro, Markus Schedl, Eva Zangerle. 2025-02-19. Unsupervised Graph Embeddings for Session-based Recommendation with Item Features. https://arxiv.org/abs/2502.13763

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