Research Article
Recommendation with quantitative implication rules
@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}, volume={6}, number={16}, 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
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.
Copyright © 2019 Hoang Tan Nguyen et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.