• DocumentCode
    3579708
  • Title

    Enhancing Context-Aware Recommendation via a Unified Graph Model

  • Author

    Hao Wu ; Xiaoxin Liu ; Yijian Pei ; Bo Li

  • Author_Institution
    Sch. of Inf. Sci. & Eng., Yunnan Univ., Kunming, China
  • fYear
    2014
  • Firstpage
    76
  • Lastpage
    79
  • Abstract
    In order to assist the development and use of context-aware recommendation capabilities, we propose a unified graph model which incorporates contextual information in an advantageous way. Basic RWR and Context RWR are specifically designed to estimate the relevance between the target user and the items against the contextual graph. Also, we propose two post-processing strategies to filter recommendation results given contextual conditions. Being dependent on experimental results on two datasets, all proposed methods are effective to enhance the quality of context-aware recommendations.
  • Keywords
    graph theory; recommender systems; ubiquitous computing; BasicRWR; ContextRWR; context-aware recommendation capabilities; context-aware recommendation enhancement; context-aware recommendations; contextual graph; filter recommendation; post-processing strategies; unified graph model; Collaboration; Context; Context modeling; Predictive models; Recommender systems; Semantics; context-aware recommendation; graph model; pervasive computing; post-filtering; random walks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Identification, Information and Knowledge in the Internet of Things (IIKI), 2014 International Conference on
  • Type

    conf

  • DOI
    10.1109/IIKI.2014.23
  • Filename
    7064002