Corpus ID: 8278616

Scalable Recommendation with Poisson Factorization

@article{Gopalan2013ScalableRW,
  title={Scalable Recommendation with Poisson Factorization},
  author={Prem Gopalan and J. Hofman and D. Blei},
  journal={ArXiv},
  year={2013},
  volume={abs/1311.1704}
}
  • Prem Gopalan, J. Hofman, D. Blei
  • Published 2013
  • Computer Science, Mathematics
  • ArXiv
  • We develop a Bayesian Poisson matrix factorization model for forming recommendations from sparse user behavior data. [...] Key Method We develop a variational inference algorithm for approximate posterior inference that scales up to massive data sets. This is an efficient algorithm that iterates over the observed entries and adjusts an approximate posterior over the user/item representations.Expand Abstract
    The Groups of Automorphisms are Complete for Free Burnside Groups of odd exponents n ≥ 1003
    7
    Understanding Users' Budgets for Recommendation with Hierarchical Poisson Factorization
    3
    Content-based recommendations with Poisson factorization
    133

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 62 REFERENCES
    Semi-explicit runge-kutta methods
    32
    Dimerization of human growth hormone by zinc.
    162
    Syntax and semantics of superintutionistic logics
    27
    Polymer translocation in a double-force arrangement
    13
    J. Appi. phys
    • 1962