5 research outputs found
Prior specification via prior predictive matching: Poisson matrix factorization and beyond
Hyperparameter optimization for machine learning models is typically carried
out by some sort of cross-validation procedure or global optimization, both of
which require running the learning algorithm numerous times. We show that for
Bayesian hierarchical models there is an appealing alternative that allows
selecting good hyperparameters without learning the model parameters during the
process at all, facilitated by the prior predictive distribution that
marginalizes out the model parameters. We propose an approach that matches
suitable statistics of the prior predictive distribution with ones provided by
an expert and apply the general concept for matrix factorization models. For
some Poisson matrix factorization models we can analytically obtain exact
hyperparameters, including the number of factors, and for more complex models
we propose a model-independent optimization procedure
Dynamic Collaborative Filtering With Compound Poisson Factorization
Model-based collaborative filtering (CF) analyzes user–item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most CF approaches assume that these latent factors are static; however, in most CF data, user preferences and item perceptions drift over time. Here, we propose a new conjugate and numerically stable dynamic matrix factorization (DCPF) based on hierarchical Poisson factorization that models the smoothly drifting latent factors using gamma-Markov chains. We propose a conjugate gamma chain construction that is numerically stable within our compound-Poisson framework. We then derive a scalable stochastic variational inference approach to estimate the parameters of our model. We apply our model to time-stamped ratings data sets from Netflix, Yelp, and Last.fm. We empirically demonstrate that DCPF achieves a higher predictive accuracy than state-of-the-art static and dynamic factorization algorithms