Producer-Fairness in Sequential Bundle Recommendation
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916810791583744 |
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| author | Rio, Alexandre Soare, Marta Amer-Yahia, Sihem |
| author_facet | Rio, Alexandre Soare, Marta Amer-Yahia, Sihem |
| contents | We address fairness in the context of sequential bundle recommendation, where users are served in turn with sets of relevant and compatible items. Motivated by real-world scenarios, we formalize producer-fairness, that seeks to achieve desired exposure of different item groups across users in a recommendation session. Our formulation combines naturally with building high quality bundles. Our problem is solved in real time as users arrive. We propose an exact solution that caters to small instances of our problem. We then examine two heuristics, quality-first and fairness-first, and an adaptive variant that determines on-the-fly the right balance between bundle fairness and quality. Our experiments on three real-world datasets underscore the strengths and limitations of each solution and demonstrate their efficacy in providing fair bundle recommendations without compromising bundle quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20329 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Producer-Fairness in Sequential Bundle Recommendation Rio, Alexandre Soare, Marta Amer-Yahia, Sihem Machine Learning Information Retrieval We address fairness in the context of sequential bundle recommendation, where users are served in turn with sets of relevant and compatible items. Motivated by real-world scenarios, we formalize producer-fairness, that seeks to achieve desired exposure of different item groups across users in a recommendation session. Our formulation combines naturally with building high quality bundles. Our problem is solved in real time as users arrive. We propose an exact solution that caters to small instances of our problem. We then examine two heuristics, quality-first and fairness-first, and an adaptive variant that determines on-the-fly the right balance between bundle fairness and quality. Our experiments on three real-world datasets underscore the strengths and limitations of each solution and demonstrate their efficacy in providing fair bundle recommendations without compromising bundle quality. |
| title | Producer-Fairness in Sequential Bundle Recommendation |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2506.20329 |