PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improved neighborhood-based algorithms for large-scale recommender systems.
Andreas Töscher, Michael Jahrer and Robert Legenstein
In: Workshop on Large Scale Recommenders Systems and the Netflix Prize, KDD 08, 25 Aug 2008, Las Vegas, USA.

Abstract

Neighborhood-based algorithms are frequently used modules of recommender systems. Usually, the choice of the similarity measure used for evaluation of neighborhood relationships is crucial for the success of such approaches. In this article we propose a way to calculate similarities by formulating a regression problem which enables us to extract the similarities from the data in a problem-specic way. Another popular approach for recommender systems is regularized matrix factorization (RMF). We present an algorithm - neighborhood-aware matrix factorization - which efficiently includes neighborhood information in a RMF model. This leads to increased prediction accuracy. The proposed methods are tested on the Net ix dataset.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:5386
Deposited By:Michael Pfeiffer
Deposited On:31 March 2009