The Bayesian Low-Rank Determinantal Point Process Mixture Model

@article{Gartrell2016TheBL,
  title={The Bayesian Low-Rank Determinantal Point Process Mixture Model},
  author={Mike Gartrell and Ulrich Paquet and Noam Koenigstein},
  journal={CoRR},
  year={2016},
  volume={abs/1608.04245}
}
Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, such as an item catalog. They are useful for a number of machine learning tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. Recent work has shown that using a low-rank factorization of this kernel provides remarkable scalability improvements that open the door to training on large-scale datasets and… CONTINUE READING
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