Personalized Contest Recommendation in Fantasy Sports
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912544963166208 |
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| author | Srilakshmi, Madiraju Kothari, Kartavya Marathe, Kamlesh Chigurupati, Vedavyas Kapoor, Hitesh |
| author_facet | Srilakshmi, Madiraju Kothari, Kartavya Marathe, Kamlesh Chigurupati, Vedavyas Kapoor, Hitesh |
| contents | In daily fantasy sports, players enter into "contests" where they compete against each other by building teams of athletes that score fantasy points based on what actually occurs in a real-life sports match. For any given sports match, there are a multitude of contests available to players, with substantial variation across 3 main dimensions: entry fee, number of spots, and the prize pool distribution. As player preferences are also quite heterogeneous, contest personalization is an important tool to match players with contests. This paper presents a scalable contest recommendation system, powered by a Wide and Deep Interaction Ranker (WiDIR) at its core. We productionized this system at our company, one of the large fantasy sports platforms with millions of daily contests and millions of players, where online experiments show a marked improvement over other candidate models in terms of recall and other critical business metrics. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_14065 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Personalized Contest Recommendation in Fantasy Sports Srilakshmi, Madiraju Kothari, Kartavya Marathe, Kamlesh Chigurupati, Vedavyas Kapoor, Hitesh Information Retrieval Machine Learning In daily fantasy sports, players enter into "contests" where they compete against each other by building teams of athletes that score fantasy points based on what actually occurs in a real-life sports match. For any given sports match, there are a multitude of contests available to players, with substantial variation across 3 main dimensions: entry fee, number of spots, and the prize pool distribution. As player preferences are also quite heterogeneous, contest personalization is an important tool to match players with contests. This paper presents a scalable contest recommendation system, powered by a Wide and Deep Interaction Ranker (WiDIR) at its core. We productionized this system at our company, one of the large fantasy sports platforms with millions of daily contests and millions of players, where online experiments show a marked improvement over other candidate models in terms of recall and other critical business metrics. |
| title | Personalized Contest Recommendation in Fantasy Sports |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2508.14065 |