Collaborative filtering or recommender sys tems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, in cluding techniques based on correlation coef ficients, vector-based similarity calculations, and statistical Bayesian methods. We com pare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evalua tion metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second esti mates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommen dation in an ordered list. Experiments were run for datasets associ ated with 3 application areas, 4 experimen tal protocols, and the 2 evaluation met rics for the various algorithms. Results indicate that for a wide range of con ditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector similarity methods. Between correlation and Bayesian networks, the preferred method de pends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other con siderations include the size of database, speed of predictions, and learning time.
Breese et al. REF describe several algorithms designed for collaborative filtering and compare their predictive accuracy in a set of representative problem domains.
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
ArXiv
ArXiv
Computer Science