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Context-aware recommendation using rough set model and collaborative filtering

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Abstract

Context has been identified as an important factor in recommender systems. Lots of researches have been done for context-aware recommendation. However, in current approaches, the weights of contextual information are the same, which limits the accuracy of the results. This paper aims to propose a context-aware recommender system by extracting, measuring and incorporating significant contextual information in recommendation. The approach is based on rough set theory and collaborative filtering. It involves a three-steps process. At first, significant attributes to represent contextual information are extracted and measured to identify recommended items based on rough set theory. Then the users’ similarity is measured in a target context consideration. Furthermore collaborative filtering is adopted to recommend appropriate items. The evaluation experiments show that the proposed approach is helpful to improve the recommendation quality.

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Authors and Affiliations

  1. College of Biomedical Engineering and Instrument Science of Zhejiang University, The Key Laboratory of Biomedical Engineering, Ministry of Education, Hangzhou, Zhejiang, China

    Zhengxing Huang, Xudong Lu & Huilong Duan

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  1. Zhengxing Huang
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  2. Xudong Lu
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  3. Huilong Duan
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Correspondence to Xudong Lu.

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Huang, Z., Lu, X. & Duan, H. Context-aware recommendation using rough set model and collaborative filtering. Artif Intell Rev 35, 85–99 (2011). https://doi.org/10.1007/s10462-010-9185-7

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  • DOI: https://doi.org/10.1007/s10462-010-9185-7

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