MARec: Metadata Alignment for cold-start Recommendation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912151304667136 |
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| author | Monteil, Julien Vaskovych, Volodymyr Lu, Wentao Majumder, Anirban Hengel, Anton van den |
| author_facet | Monteil, Julien Vaskovych, Volodymyr Lu, Wentao Majumder, Anirban Hengel, Anton van den |
| contents | For many recommender systems, the primary data source is a historical record of user clicks. The associated click matrix is often very sparse, as the number of users x products can be far larger than the number of clicks. Such sparsity is accentuated in cold-start settings, which makes the efficient use of metadata information of paramount importance. In this work, we propose a simple approach to address cold-start recommendations by leveraging content metadata, Metadata Alignment for cold-start Recommendation. We show that this approach can readily augment existing matrix factorization and autoencoder approaches, enabling a smooth transition to top performing algorithms in warmer set-ups. Our experimental results indicate three separate contributions: first, we show that our proposed framework largely beats SOTA results on 4 cold-start datasets with different sparsity and scale characteristics, with gains ranging from +8.4% to +53.8% on reported ranking metrics; second, we provide an ablation study on the utility of semantic features, and proves the additional gain obtained by leveraging such features ranges between +46.8% and +105.5%; and third, our approach is by construction highly competitive in warm set-ups, and we propose a closed-form solution outperformed by SOTA results by only 0.8% on average. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_13298 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MARec: Metadata Alignment for cold-start Recommendation Monteil, Julien Vaskovych, Volodymyr Lu, Wentao Majumder, Anirban Hengel, Anton van den Information Retrieval Systems and Control For many recommender systems, the primary data source is a historical record of user clicks. The associated click matrix is often very sparse, as the number of users x products can be far larger than the number of clicks. Such sparsity is accentuated in cold-start settings, which makes the efficient use of metadata information of paramount importance. In this work, we propose a simple approach to address cold-start recommendations by leveraging content metadata, Metadata Alignment for cold-start Recommendation. We show that this approach can readily augment existing matrix factorization and autoencoder approaches, enabling a smooth transition to top performing algorithms in warmer set-ups. Our experimental results indicate three separate contributions: first, we show that our proposed framework largely beats SOTA results on 4 cold-start datasets with different sparsity and scale characteristics, with gains ranging from +8.4% to +53.8% on reported ranking metrics; second, we provide an ablation study on the utility of semantic features, and proves the additional gain obtained by leveraging such features ranges between +46.8% and +105.5%; and third, our approach is by construction highly competitive in warm set-ups, and we propose a closed-form solution outperformed by SOTA results by only 0.8% on average. |
| title | MARec: Metadata Alignment for cold-start Recommendation |
| topic | Information Retrieval Systems and Control |
| url | https://arxiv.org/abs/2404.13298 |