Text retrieval methods for item ranking in collaborative filtering

Authors:
Alejandro Bellogín;Jun Wang;Pablo Castells
Affiliations:
Universidad Autónoma de Madrid, Escuela Politécnica Superior, Madrid, Spain;Department of Computer Science, University College London, London, UK;Universidad Autónoma de Madrid, Escuela Politécnica Superior, Madrid, Spain
Venue:
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Year:
2011

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Abstract

Collaborative Filtering (CF) aims at predicting unknown ratings of a user from other similar users. The uniqueness of the problem has made its formulation distinctive to other information retrieval problems. While the formulation has proved to be effective in rating prediction tasks, it has limited the potential connections between these algorithms and Information Retrieval (IR) models. In this paper we propose a common notational framework for IR and rating-based CF, as well as a technique to provide CF data with a particular structure, in order to be able to use any IR weighting function with it. We argue that the flexibility of our approach may yield to much better performing algorithms. In fact, in this work we have found that IR models perform well in item ranking tasks, along with different normalization strategies.