Context-Aware Systems and Applications, and Nature of Computation and Communication. 7th EAI International Conference, ICCASA 2018, and 4th EAI International Conference, ICTCC 2018, Viet Tri City, Vietnam, November 22–23, 2018, Proceedings

Research Article

Context-Aware Recommendation with Objective Interestingness Measures

  • @INPROCEEDINGS{10.1007/978-3-030-06152-4_13,
        author={Nghi Pham and Nghia Phan and Dang Dang and Hiep Huynh},
        title={Context-Aware Recommendation with Objective Interestingness Measures},
        proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 7th EAI International Conference, ICCASA 2018, and 4th EAI International Conference, ICTCC 2018, Viet Tri City, Vietnam, November 22--23, 2018, Proceedings},
        proceedings_a={ICCASA \& ICTCC},
        year={2019},
        month={1},
        keywords={Rating matrix Context similarity matrix Objective interestingness measures Chi-square similarity kernel},
        doi={10.1007/978-3-030-06152-4_13}
    }
    
  • Nghi Pham
    Nghia Phan
    Dang Dang
    Hiep Huynh
    Year: 2019
    Context-Aware Recommendation with Objective Interestingness Measures
    ICCASA & ICTCC
    Springer
    DOI: 10.1007/978-3-030-06152-4_13
Nghi Pham1,*, Nghia Phan2,*, Dang Dang3,*, Hiep Huynh4,*
  • 1: Thapmuoi Vocational School
  • 2: Travinh University
  • 3: Business Center VNPT Dongthap
  • 4: Cantho University
*Contact email: phammongnghi1988@gmail.com, nghiatvnt@tvu.edu.vn, dangdv.dtp@vnpt.vn, hxhiep@ctu.edu.vn

Abstract

Context-aware recommender systems researches now concentrate on adjusting recommendation results for situations specific context of the users. These studies suggest many ways to integrate user contextual information into the recommendation process such as using topic hierarchies with matrix factorization techniques to improve context-aware recommender systems, measuring frequency-based similarity for context-aware recommender systems, collecting data from social networking to support context-aware recommender systems, and so on. However, these studies mainly focus on the development of context-aware recommendation algorithms to propose items to users in a particular situation and do not care about the extent of contextual involvement in the recommendation process to make recommendation results. In this article, we propose a new approach for context-aware recommender systems based on objective interestingness measures to consider the contextual relationship of the users in the recommendation process. Based on the experimental results on two standard datasets, the proposed model is more accurate than the traditional models.