Recommendation with quantitative implication rules
Journal Title: EAI Endorsed Transactions on Context-aware Systems and Applications - Year 2019, Vol 6, Issue 16
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.
Authors and Affiliations
Hoang Tan Nguyen, Lan Phuong Phan, Hung Huu Huynh, Hiep Xuan Huynh
Keywords
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- EP ID EP45813
- DOI http://dx.doi.org/10.4108/eai.13-7-2018.156837
- Views 240
- Downloads 0
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