Low-Rank Factorization of Determinantal Point Processes for Recommendation

  title={Low-Rank Factorization of Determinantal Point Processes for Recommendation},
  author={Mike Gartrell and Ulrich Paquet and Noam Koenigstein},
Determinantal point processes (DPPs) have garnered attention as an elegant probabilistic model of set diversity. They are useful for a number of subset selection tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. In this work we present a new method for learning the DPP kernel from observed data using a low-rank factorization of this kernel. We show that this low-rank factorization enables a learning algorithm that is nearly an order of… CONTINUE READING
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