1998 EmpiricalAnalysisofPredAlgsForCollabFilt
- (Breese et al., 1998) ⇒ John S. Breese, David Heckerman, and Carl Kadie. (1998). “Empirical Analysis of Predictive Algorithms for Collaborative Filtering." In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI 1998).
Subject Headings: Collaborative Filtering Algorithm.
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Abstract
Collaborative filtering or recommender systems 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, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metrics for the various algorithms. Results indicate that for a wide range of conditions, 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 depends 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 considerations include the size of database, speed of predictions, and learning time.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1998 EmpiricalAnalysisofPredAlgsForCollabFilt | John S. Breese David Heckerman Carl Kadie | Empirical Analysis of Predictive Algorithms for Collaborative Filtering | http://research.microsoft.com/~carlk/papers/cfalg.htm |