What have the neighbours ever done for us? A collaborative filtering perspective
|Title:||What have the neighbours ever done for us? A collaborative filtering perspective||Authors:||Rafter, Rachael
O'Mahony, Michael P.
Hurley, Neil J.
|Permanent link:||http://hdl.handle.net/10197/1336||Date:||Jun-2009||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 systems;Collaborative filtering;Predictive accuracy||Subject LCSH:||Recommender systems (Information filtering)
Information filtering systems
|DOI:||10.1007/978-3-642-02247-0_36||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|
|Appears in Collections:||CLARITY Research Collection|
Computer Science Research Collection
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