International Conference on User Modeling, Adaptation, and Personalization

UMAP 2015: User Modeling, Adaptation and Personalization pp 370-376

Integrating Context Similarity with Sparse Linear Recommendation Model

Conference paper

DOI: 10.1007/978-3-319-20267-9_33

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9146)
Cite this paper as:
Zheng Y., Mobasher B., Burke R. (2015) Integrating Context Similarity with Sparse Linear Recommendation Model. In: Ricci F., Bontcheva K., Conlan O., Lawless S. (eds) User Modeling, Adaptation and Personalization. UMAP 2015. Lecture Notes in Computer Science, vol 9146. Springer, Cham

Abstract

Context-aware recommender systems extend traditional recommender systems by adapting their output to users’ specific contextual situations. Most of the existing approaches to context-aware recommendation involve directly incorporating context into standard recommendation algorithms (e.g., collaborative filtering, matrix factorization). In this paper, we highlight the importance of context similarity and make the attempt to incorporate it into context-aware recommender. The underlying assumption behind is that the recommendation lists should be similar if their contextual situations are similar. We integrate context similarity with sparse linear recommendation model to build a similarity-learning model. Our experimental evaluation demonstrates that the proposed model is able to outperform several state-of-the-art context-aware recommendation algorithms for the top-N recommendation task.

Keywords

Context Context-aware recommendation Context similarity 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computing, Center for Web IntelligenceDePaul UniversityChicagoUSA

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