Unsupervised Graph Embeddings for Session-based Recommendation with Item Features
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929753979617280 |
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| author | Peintner, Andreas Moscati, Marta Parada-Cabaleiro, Emilia Schedl, Markus Zangerle, Eva |
| author_facet | Peintner, Andreas Moscati, Marta Parada-Cabaleiro, Emilia Schedl, Markus Zangerle, Eva |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13763 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unsupervised Graph Embeddings for Session-based Recommendation with Item Features Peintner, Andreas Moscati, Marta Parada-Cabaleiro, Emilia Schedl, Markus Zangerle, Eva Information Retrieval 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%. |
| title | Unsupervised Graph Embeddings for Session-based Recommendation with Item Features |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2502.13763 |