Download "重排04：DPP 多样性算法（上）"
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行列式点过程 (determinantal point process, DPP) 是一种经典的机器学习方法，在 1970's 年代提出，在 2000 年之后有快速的发展。DPP 是目前推荐系统重排多样性公认的最好方法。 DPP 的数学比较复杂，这节课先介绍数学基础，下节课再介绍它在推荐系统的应用。这节课的内容主要是超平行体、超平行体的体积、行列式与体积的关系。 参考文献： Chen et al. Fast greedy map inference for determinantal point process to improve recommendation diversity. In NIPS, 2018. 课件和讲义： https://github.com/wangshusen/RecommenderSystem
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