casa 14(1): e3

Research Article

Enrichment of Multi-criteria Communities for Context-aware Recommendations

Download1029 downloads
  • @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},
        keywords={collaborative filtering, context-aware recommender system, matrix factorization; multi-criteria communities},
  • Thuy Ngoc Nguyen
    An Te Nguyen
    Year: 2014
    Enrichment of Multi-criteria Communities for Context-aware Recommendations
    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:


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.