International Conference on Web Information Systems Engineering

Web Information Systems Engineering – WISE 2015 pp 431-447 | Cite as

Similarity-Based Context-Aware Recommendation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9418)

Abstract

Context-aware recommender systems (CARS) take context into consideration when modeling user preferences. There are two general ways to integrate context with recommendation: contextual filtering and contextual modeling. Currently, the most effective context-aware recommendation algorithms are based on a contextual modeling approach that estimate deviations in ratings across different contexts. In this paper, we propose context similarity as an alternative contextual modeling approach and examine different ways to represent context similarity and incorporate it into recommendation. More specifically, we show how context similarity can be integrated into the sparse linear method and matrix factorization algorithms. Our experimental results demonstrate that learning context similarity is a more effective approach to context-aware recommendation than modeling contextual rating deviations.

Keywords

Recommender system Context Context-aware Matrix factorization 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Center for Web Intelligence, School of ComputingDePaul UniversityChicagoUSA

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