Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2009

Abstract

Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach.

Keywords

Recommender Systems, Collaborative Filtering, Experts, Cosine Similarity, Nearest Neighbors, Top-N Recommendations

Discipline

Numerical Analysis and Scientific Computing | Theory and Algorithms

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, Boston, MA, United States, July 19-23

First Page

532

Last Page

539

ISBN

978-1-60558-483-6

Identifier

10.1145/1571941.1572033

Publisher

ACM

City or Country

New York

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