为虚拟体育赛事玩家精准推荐参赛比赛，提升参与度。
Personalized Contest Recommendation in Fantasy Sports
- 采用宽深交互排序模型（WiDIR）实现个性化推荐
- 线上实验显示召回率等核心指标显著提升
- 适合大规模虚拟体育平台的实时推荐场景
在每日虚拟体育赛事中，玩家通过组建运动员队伍参赛，其得分基于真实比赛表现。每场体育比赛均提供大量参赛选项，差异体现在入场费、晋级名额和奖金分配三个维度。由于玩家偏好差异显著，个性化推荐对匹配用户与赛事至关重要。本文提出一种可扩展的参赛推荐系统，核心为宽深交互排序模型（WiDIR）。该系统已在一家拥有数百万日活比赛和数百万玩家的大型虚拟体育平台上线，线上实验表明，其在召回率及其他关键业务指标上均显著优于其他候选模型。
原文摘要 · Abstract (English)
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.
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