Differential Context Relaxation for Context-Aware Travel Recommendation

  • Yong Zheng
  • Robin Burke
  • Bamshad Mobasher
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 123)

Abstract

Context-aware recommendation (CARS) has been shown to be an effective approach to recommendation in a number of domains. However, the problem of identifying appropriate contextual variables remains: using too many contextual variables risks a drastic increase in dimensionality and a loss of accuracy in recommendation. In this paper, we propose a novel treatment of context – identifying influential contexts for different algorithm components instead of for the whole algorithm. Based on this idea, we take traditional user-based collaborative filtering (CF) as an example, decompose it into three context-sensitive components, and propose a hybrid contextual approach. We then identify appropriate relaxations of contextual constraints for each algorithm component. The effectiveness of context relaxation is demonstrated by comparison of three algorithms using a travel data set: a contenxt-ignorant approach, contextual pre-filtering, and our hybrid contextual algorithm. The experiments show that choosing an appropriate relaxation of the contextual constraints for each component of an algorithm outperforms strict application of the context.

Keywords

Context-aware Recommender System Collaborative Filtering Contextual Pre-Filtering Context Relaxation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yong Zheng
    • 1
  • Robin Burke
    • 1
  • Bamshad Mobasher
    • 1
  1. 1.Center for Web Intelligence School of ComputingDePaul UniversityChicagoUSA

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