barcode, an additive Poisson latent-factor model, infers binary and continuous sample and species features and identifies six interpretable environmental drivers of Finnish bird abundances.
Fast Non-Bayesian Poisson Factorization for Implicit-Feedback Recommendations
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
This work explores non-negative low-rank matrix factorization based on regularized Poisson models (PF or "Poisson factorization" for short) for recommender systems with implicit-feedback data. The properties of Poisson likelihood allow a shortcut for very fast computations over zero-valued inputs, and oftentimes results in very sparse factors for both users and items. Compared to HPF (a popular Bayesian formulation of the problem with hierarchical priors), the frequentist optimization-based approach presented here tends to produce better top-N recommendations with significantly shorter fitting times, on top of having sparse solutions.
fields
stat.AP 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Joint species distribution modeling of abundance data through latent variable barcodes
barcode, an additive Poisson latent-factor model, infers binary and continuous sample and species features and identifies six interpretable environmental drivers of Finnish bird abundances.