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World Wide Web

, Volume 18, Issue 5, pp 1351–1371 | Cite as

A Graph-based model for context-aware recommendation using implicit feedback data

  • Weilong YaoEmail author
  • Jing He
  • Guangyan Huang
  • Jie Cao
  • Yanchun Zhang
Article

Abstract

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.

Keywords

Recommendation Graph model Context Semantics Implicit feedback 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Weilong Yao
    • 1
    Email author
  • Jing He
    • 2
  • Guangyan Huang
    • 3
  • Jie Cao
    • 4
  • Yanchun Zhang
    • 2
  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.Centre for Applied InformaticsVictoria UniversityMelbourneAustralia
  3. 3.Deakin UniversityMelbourneAustralia
  4. 4.Nanjing University of Finance and EconomicsNanjingChina

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