Recommendation with Differential Context Weighting

  • Yong Zheng
  • Robin Burke
  • Bamshad Mobasher
Conference paper

DOI: 10.1007/978-3-642-38844-6_13

Volume 7899 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Zheng Y., Burke R., Mobasher B. (2013) Recommendation with Differential Context Weighting. In: Carberry S., Weibelzahl S., Micarelli A., Semeraro G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg

Abstract

Context-aware recommender systems (CARS) adapt their recommendations to users’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called differential context relaxation (DCR), in which different subsets of contextual features were applied in different components of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach – differential context weighting (DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm optimization (PSO) can be used to efficiently determine the weights for DCW.

Keywords

recommender systems collaborative filtering context context-aware recommendation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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