Supervised and Active Learning for Recommender Systems
Laurent Charlin · TSpace (University of Toronto) · 2014
Traditional approaches to recommender systems have often focused on the collaborative filtering problem: using users' past preferences in order to predict their future preferences. Although essential, rating prediction is only one of the components of a successful recommender system. One important problem is how to translate predicted ratings into actual recommendations. Furthermore, considering additional information either about users or items may offer substantial gains in performance while allowing the system to provide good recommendations to new users.We develop machine learning methods in response to some of the limitations of current recommender-systems' research. Specifically, we propose a three-stage framework to model recommender systems.We first propose an elicitation step which serves as a way to collect user information beneficial to the recommendation task. In this thesis we framed the elicitation process as one of active learning. We developed several active elicitation methods which, unlike previous approaches which exclusively focus improving the learning model, directly aim at improving the recommendation objective.The second stage of our framework uses the elicited user information to inform models that predict user-item preferences. We focus user-preference prediction for a document recommendation problem for which we introduce a novel graphical model over the space of user side-information, item (document) contents, and user-item preferences. Our model is able to smoothly tradeoff its usage of side information and of user-item preferences to make good document recommendations in both cold-start and non-cold-start data regimes.The final step of our framework consists of the recommendation procedure. In particular, we focus on a matching instantiation and explore different natural matching objectives and constraints for the paper- to-reviewer matching problem. Further, we explore and analyze the synergy between the recommendation objective and the learning objective.In all stages of our work we experimentally validate our models on a variety of datasets from different domains. Of particular interest are several datasets containing reviewer preferences about papers submitted to conferences. These datasets were collected using the Toronto Paper Matching System, a system we built to help conference organizers in the task of matching reviewers to submitted papers.