Poisson Matrix Factorization

Recommendation systems are a vital component of the modern web. They help readers effectively navigate otherwise unwieldy archives of information and help websites direct users to items - movies, articles, songs, products that they will like. Collaborative filtering is one of the techniques used for building recommendation systems which involves inferring user preferences and item attributes from data.
In this project, I studied various models for Bayesian Recommender Systems including Poisson Matrix Factorization and its extensions like Hierarchical Poisson Matrix Factorization and Bayesian Non-parametric Poisson Matrix Factorization. I analyzed the effect of latent dimensions on the models and learnt the use of auxiliary variables in variational inference to make the models locally conjugate and facilitate inference. I also evaluated their performance on MovieLens 1M dataset.