Interpreting Contextual Effects By Contextual Modeling In Recommender Systems

Preprint English OPEN
Zheng, Yong;
(2017)
  • Subject: Computer Science - Information Retrieval

Recommender systems have been widely applied to assist user's decision making by providing a list of personalized item recommendations. Context-aware recommender systems (CARS) additionally take context information into considering in the recommendation process, since u... View more
  • References (20)
    20 references, page 1 of 2

    [1] Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin. 2011. Context-Aware Recommender Systems. AI Magazine 32, 3 (2011), 67-80.

    [2] Gediminas Adomavicius and Alexander Tuzhilin. 2011. Context-aware recommender systems. In Recommender systems handbook. Springer, 217-253.

    [3] Linas Baltrunas, Marius Kaminskas, Bernd Ludwig, Omar Moling, Francesco Ricci, Aykan Aydin, Karl-Heinz Lüke, and Roland Schwaiger. 2011. Incarmusic: Contextaware music recommendations in a car. In E-Commerce and Web Technologies. Springer, 89-100.

    [4] Linas Baltrunas, Bernd Ludwig, and Francesco Ricci. 2011. Matrix factorization techniques for context aware recommendation. In Proceedings of the fth ACM conference on Recommender systems. ACM, 301-304.

    [5] Linas Baltrunas and Francesco Ricci. 2009. Context-based splitting of item ratings in collaborative ltering. In Proceedings of ACM conference on Recommender systems. 245-248.

    [6] Matthias Braunhofer, Mehdi Elahi, Francesco Ricci, and Thomas Schievenin. 2013. Context-Aware Points of Interest Suggestion with Dynamic Weather Data Management. In Information and Communication Technologies in Tourism 2014. Springer, 87-100.

    [7] Victor Codina, Francesco Ricci, and Luigi Ceccaroni. 2013. Exploiting the semantic similarity of contextual situations for pre- ltering recommendation. In User Modeling, Adaptation, and Personalization. Springer, 165-177.

    [8] Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, and Nuria Oliver. 2010. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative ltering. In Proceedings of the fourth ACM conference on Recommender systems. ACM, 79-86.

    [9] Xia Ning and George Karypis. 2011. SLIM: Sparse linear methods for top-n recommender systems. In Data Mining (ICDM), 2011 IEEE 11th International Conference on. IEEE, 497-506.

    [10] Umberto Panniello, Alexander Tuzhilin, Michele Gorgoglione, Cosimo Palmisano, and Anto Pedone. 2009. Experimental comparison of pre-vs. post- ltering approaches in context-aware recommender systems. In Proceedings of the third ACM conference on Recommender systems. ACM, 265-268.

  • Related Research Results (1)
    Inferred by OpenAIRE
    software
    CARSKit software on GitHub
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