Unsupervised Graph Embeddings for Session-based Recommendation with Item Features

• URL: http://arxiv.org/abs/2502.13763v1
• Date: Wed, 19 Feb 2025 14:23:18 GMT
• Title: Unsupervised Graph Embeddings for Session-based Recommendation with Item Features
• Authors: Andreas Peintner, Marta Moscati, Emilia Parada-Cabaleiro, Markus Schedl, Eva Zangerle,
• Abstract summary: In session-based recommender systems, predictions are based on the user's preceding behavior in the session. We propose a novel method, Graph Convolutional Network Extension (GCNext), which incorporates item features directly into the graph representation. Our flexible extension is easy to incorporate in state-of-the-art methods and increases the MRR@20 by up to 12.79%.
• Score: 10.067724849703321
• License:
• 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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