casa 18: e1

Research Article

Recommendation with quantitative implication rules

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  • @ARTICLE{10.4108/eai.13-7-2018.156837,
        author={Hoang Tan Nguyen and Lan Phuong Phan and Hung Huu Huynh and Hiep Xuan Huynh},
        title={Recommendation with quantitative implication rules},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={CASA},
        year={2019},
        month={3},
        keywords={association rules, implication rules, quantitative dataset, recommendation},
        doi={10.4108/eai.13-7-2018.156837}
    }
    
  • Hoang Tan Nguyen
    Lan Phuong Phan
    Hung Huu Huynh
    Hiep Xuan Huynh
    Year: 2019
    Recommendation with quantitative implication rules
    CASA
    EAI
    DOI: 10.4108/eai.13-7-2018.156837
Hoang Tan Nguyen1, Lan Phuong Phan2, Hung Huu Huynh3, Hiep Xuan Huynh2,*
  • 1: Department of Information and Communications of Dong Thap province, Vietnam
  • 2: Cantho University, 3/2 Street, Ninh Kieu District, Cantho City, Vietnam
  • 3: Danang University of Science and Technology, Nguyen Luong Bang St, Danang City, Vietnam
*Contact email: hxhiep@ctu.edu.vn

Abstract

Association rules based recommendation is one of approaches to develop recommendation systems. However, such systems just focus on binary dataset, whereas many datasets are in the quantitative form. There are many solutions proposed for this problem such as combining the association rules mining with fuzzy logic, binarizing quantitative data, etc. These proposals have contributed to improving the performance of traditional association rules mining, however, they have to deal with the trade-off between the processing performance and the loss of information. In this paper, we propose a new approach to make recommendations based on implication rules. The experimental results show that our proposed solution can be implemented on quantitative dataset well as well as improve the accuracy and performance of the recommendation systems.