HE Ming, DONG Tao, LIU Yi. A Heuristic Approach for Context-Aware Recommendation Using Rough Set Theory[J]. Chinese Journal of Electronics, 2018, 27(3): 500-506. doi: 10.1049/cje.2018.03.016
Citation: HE Ming, DONG Tao, LIU Yi. A Heuristic Approach for Context-Aware Recommendation Using Rough Set Theory[J]. Chinese Journal of Electronics, 2018, 27(3): 500-506. doi: 10.1049/cje.2018.03.016

A Heuristic Approach for Context-Aware Recommendation Using Rough Set Theory

doi: 10.1049/cje.2018.03.016
Funds:  This work is supported by the Natural Science Foundation of Beijing (No.4153058).
  • Received Date: 2015-10-10
  • Rev Recd Date: 2015-11-16
  • Publish Date: 2018-05-10
  • Context-aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. However, not all contextual information might be relevant or useful for recommendation purposes, and little work has been done on measuring how important the contextual information for recommendation. We propose a heuristic optimization algorithm based on rough set theory and collaborative filtering to using contextual information more efficiently for boosting recommendation. Our approach involves three processes. First, significant attributes to represent contextual information are extracted and measured to identify recommended items using rough set theory. Second, the user similarity is evaluated in a target context consideration. Third, collaborative filtering is applied to recommend appropriate items. We perform an empirical comparison of three approaches on two real-world data sets. The experimental results show that the proposed approach generates more accurate predictions.
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  • G. Adomavicius, B. Mobasher, F. Ricci, et al., "Context-aware recommender systems", AI Magazine, Vol.32, No.3, pp.67-80, 2011.
    A.Q.Macedo, L.B. Marinho and RL.T. Santos, "Context-aware event recommendation in event-based social networks", Proc. of the 9th ACM Conference on Recommender Systems (RecSys'15), Vienna, Austria, pp.123-130, 2015.
    N. Hariri, B. Mobasher, R. Burke, et al., "Context-aware recommendation based on review mining", the Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, International Joint Conferences on Artificial Intelligence, pp.1-7, 2011.
    A. Said, E.W. De Luca and S. Albayrak, "Inferring contextual user profiles-Improving recommender performance", Proc. of the 4th Workshop on Context-Aware Recommender Systems, Dublin, Ireland, pp.1-5, 2011.
    Y. Zheng, R. Burke and B. Mobasher, "Recommendation with differential context weighting", The 21st Conference on User Modeling, Adaptation and Personalization, Rome, Italy, pp.152-164, 2013.
    A.K. Dey, "Understanding and using context", Personal Ubiquitous Comput., Vol.5, No.1, pp.4-7, 2001.
    G. Adomavicius, A. Tuzhilin, R. Sankaranarayanan, et al., "Incorporating contextual information in recommender systems using a multidimensional approach", ACM Trans. Information Systems, Vol.23, No.1, pp.103-145, 2005.
    Z. Pawalk, "Rough sets", International Journal of Computer and Information Science, Vol.11, No.5, pp.341-356, 1982.
    Z. Pawalk, "Rough sets and intelligent data analysis", Information Sciences, Vol.147, pp.1-12, 2002.
    Z.X. Huang, X.D. Lu and H.L. Duan, "Context-aware recommendation using rough set model and collaborative filtering", Artificial Intelligence Review, Vol.35, No.1, pp.85-99, 2010.
    Z. Gantner, S. Rendle, C. Freudenthaler, et al., "Mymedialite:A free recommender system library", Proc. of ACM Conference on Recommender Systems, ACM, pp.305-308, 2011.
    A. Karatzoglou, X. Amatriain, L. Baltrunas, et al., "Multiverse recommendation:n-Dimensional tensor factorization for context-aware collaborative filtering", Proc. of the 4th ACM Conference on Recommender Systems, pp.79-86, 2010.
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