What have the neighbours ever done for us? A collaborative filtering perspective

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Title: What have the neighbours ever done for us? A collaborative filtering perspective
Authors: Rafter, Rachael
O'Mahony, Michael P.
Hurley, Neil J.
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/1336
Date: Jun-2009
Online since: 2009-08-05T15:46:01Z
Abstract: Collaborative filtering (CF) techniques have proved to be a powerful and popular component of modern recommender systems. Common approaches such as user-based and item-based methods generate predictions from the past ratings of users by combining two separate ratings components: a base estimate, generally based on the average rating of the target user or item, and a neighbourhood estimate, generally based on the ratings of similar users or items. The common assumption is that the neighbourhood estimate gives CF techniques a considerable edge over simpler average-rating techniques. In this paper we examine this assumption more carefully and demonstrate that the influence of neighbours can be surprisingly minor in CF algorithms, and we show how this has been disguised by traditional approaches to evaluation, which, we argue, have limited progress in the field.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): Springer-Verlag Berlin Heidelberg 2009
Keywords: Recommender systemsCollaborative filteringPredictive accuracy
Subject LCSH: Recommender systems (Information filtering)
Information filtering systems
DOI: 10.1007/978-3-642-02247-0_36
Other versions: The original publication is available at www.springerlink.com
Language: en
Status of Item: Peer reviewed
Is part of: Houben, G.-J. ...[et al.] (eds.). User Modeling, Adaptation, and Personalization : 17th International Conference, UMAP 2009 formerly UM and AH Trento, Italy, June 22-26, 2009 : Proceedings
Conference Details: Paper presented at the First and Seventeenth International Conference on User Modeling, Adaptation and Personalization (UMAP 09), Trento, Italy, 22-26 June, 2009
ISBN: 9783642022463
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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