Conjugate-Prior-Regularized Multinomial pLSA for Collaborative Filtering

Abstract: Collaborative filtering is a method for making predictions about consumer interests by collecting preferences or information about opinions from other consumers. For this purpose statistical modeling techniques are applied to learn personalized models for each consumer based on every purchase or provided rating to the available items. Such a technique is probabilistic Latent Semantic Analysis (pLSA), which within this thesis attempts to model consumers into groups based on similarities in movie preferences to improve personalized rating predictions on unseen movies. The main challenge with pLSA in collaborative filtering is the overfitting problem, which results in model parameters that are strictly determined by the past ratings and thus gives unreliable predictions for unknown data. To counteract the overfitting a regularization method called conjugate-prior-regularization is proposed to introduce additional information about the proportions of the model parameters. It is shown that the proposed regularization provides more robust learning from sparse datasets and also improves the recommendation performance on discrete ratings.