casa 14(1): e3

Research Article

Enrichment of Multi-criteria Communities for Context-aware Recommendations

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  • @ARTICLE{10.4108/casa.1.1.e3,
        author={Thuy Ngoc Nguyen and An Te Nguyen},
        title={Enrichment of Multi-criteria Communities for Context-aware Recommendations},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        volume={1},
        number={1},
        publisher={ICST},
        journal_a={CASA},
        year={2014},
        month={9},
        keywords={collaborative filtering, context-aware recommender system, matrix factorization; multi-criteria communities},
        doi={10.4108/casa.1.1.e3}
    }
    
  • Thuy Ngoc Nguyen
    An Te Nguyen
    Year: 2014
    Enrichment of Multi-criteria Communities for Context-aware Recommendations
    CASA
    ICST
    DOI: 10.4108/casa.1.1.e3
Thuy Ngoc Nguyen1,*, An Te Nguyen2
  • 1: Faculty of Information Technology, HoChiMinh City University of Pedagogy, 280 An Duong Vuong St., HCMC, Vietnam
  • 2: Computer Science Center, HoChiMinh City University of Science, 227 Nguyen Van Cu St., HCMC, Vietnam
*Contact email: ngocnt@hcmup.edu.vn

Abstract

Recommender systems are designed to help users alleviate the information overload problem by offering personalized recommendations. Most systems apply collaborative filtering to predict individual preferences based on opinions of like-minded people through their ratings on items. Recently, context-aware recommender systems (CARSs) are developed to offer users more suitable recommendations by exploiting additional context data such as time, location, etc. However, most CARSs use only ratings as a criterion for building communities, and ignore other available data allowing users to be grouped into communities. This paper presents a novel approach for exploiting multi-criteria communities to provide context-aware recommendations. The main idea of the proposed algorithm is that for a given context, the significance of multi-criteria communities could be different. So communities from the most suitable criteria followed by a learning phase are incorporated into the recommendation process.