Empirical Study of Recommender Systems Using Linear Classifiers

Abstract

Recommender systems use historical data on user preferences and other available data on users (e.g., demographics) and items (e.g., taxonomy) to predict items a new user might like. Applications of these methods include recommending items for purchase and personalizing the browsing experience on a web-site. Collaborative filtering methods have focused on using just the history of user preferences to make the recommendations. These methods have been categorized as memory-based if they operate over the entire data to make predictions and as model-based if they use the data to build a model which is then used for predictions. In this paper, we propose the use of linear classifiers in a model-based recommender system. We compare our method with another model-based method using decision trees and with memory-based methods using data from various domains. Our experimental results indicate that these linear models are well suited for this application. They outperform the commonly proposed approach using a memory-based method in accuracy and also have a better tradeoff between off-line and on-line computational requirements.