用个性化确定性点过程实现文化活动推荐的多样性与相关性平衡
Diversified recommendations of cultural activities with personalized determinantal point processes
- 基于用户偏好调整相似度核，提升推荐相关性
- 在线下和线上实验中验证了多样性和相关性的权衡关系
- 适合希望提升文化类内容推荐多样性的产品团队
尽管优化推荐系统以提升用户参与度是业界常规做法，但在不损害核心业务指标的前提下有效实现推荐多样化仍是一大挑战。为促进用户文化实践的多元化，本研究探索使用个性化确定性点过程（Personalized DPPs）来生成既相关又多样的推荐。我们采用经典的相似度核质量-多样性分解方法，增强对用户偏好的权重。本文展示了个性化DPP采样的具体实现，通过离线与在线指标评估相关性与多样性之间的权衡，并为生产环境中的应用提供实践洞见。为确保可复现性，我们已在GitHub公开平台及实验的完整代码。
原文摘要 · Abstract (English)
While optimizing recommendation systems for user engagement is a well-established practice, effectively diversifying recommendations without negatively impacting core business metrics remains a significant industry challenge. In line with our initiative to broaden our audience's cultural practices, this study investigates using personalized Determinantal Point Processes (DPPs) to sample diverse and relevant recommendations. We rely on a well-known quality-diversity decomposition of the similarity kernel to give more weight to user preferences. In this paper, we present our implementations of the personalized DPP sampling, evaluate the trade-offs between relevance and diversity through both offline and online metrics, and give insights for practitioners on their use in a production environment. For the sake of reproducibility, we release the full code for our platform and experiments on GitHub.
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