Context-Aware Recommendation with Objective Interestingness Measures

  • Nghi Mong PhamEmail author
  • Nghia Quoc Phan
  • Dang Van Dang
  • Hiep Xuan Huynh
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 266)


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.


Rating matrix Context similarity matrix Objective interestingness measures Chi-square similarity kernel 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Nghi Mong Pham
    • 1
    Email author
  • Nghia Quoc Phan
    • 2
  • Dang Van Dang
    • 3
  • Hiep Xuan Huynh
    • 4
  1. 1.Thapmuoi Vocational SchoolDongthapVietnam
  2. 2.Travinh UniversityTravinh CityVietnam
  3. 3.Business Center VNPT DongthapDongthapVietnam
  4. 4.Cantho UniversityCanthoVietnam

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