STAR: Sparse Text Approach for Recommendation
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| Publication date | 2024 |
| Book title | CIKM '24 |
| Book subtitle | Proceedings of the 33rd ACM International Conference on Information and Knowledge Management : October, 21-25. 2024, Boise, ID, USA |
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| Event | 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024 |
| Pages (from-to) | 4086–4090 |
| Publisher | New York, NY: Association for Computing Machinery |
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| Abstract | In this work we propose to adapt Learned Sparse Retrieval, an emerging approach in IR, to text-centric content-based recommendations, leveraging the strengths of transformer models for an efficient and interpretable user-item matching. We conduct extensive experiments, showing that our LSR-based recommender, dubbed STAR, outperforms existing dense bi-encoder baselines on three recommendation domains. The obtained word-level representations of users and items are easy to examine and result in over 10x more compact indexes. |
| Document type | Conference contribution |
| Language | English |
| Published at | https://doi.org/10.1145/3627673.3679999 |
| Downloads |
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