A novel hybrid based recommendation system based on clustering and association mining

S. Pandya*, J. Shah, N. Joshi, H. Ghayvat, S. C. Mukhopadhyay, M. H. Yap

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

36 Citations (Scopus)


In recent years, E-commerce had made a tremendous impact on the world. However before the emergence of E-commerce, individuals can't skim the information about the products within short time of the period, so therefore recommendation system was introduced. The principle point of the recommendation system is to prescribe the most appropriate items to the user. Many of the recommendation systems mainly use content based method, collaborative filtering method, demographic based method and hybrid method. In this paper, the major challenges such as 'data sparsity' and 'cold start problem' are addressed. To overcome these challenges, we propose a new methodology by combining the clustering algorithm with Eclat Algorithm for better rules generation. Firstly we cluster the rating matrix based on the user similarity. Then we convert the clustered data into Boolean data and applying Eclat Algorithm on Boolean data efficient rules generation takes place. At last based on rules generation recommendation takes place. Our experiments shows that approach not only decrease the sparsity level but also increase the accuracy of a system.

Original languageEnglish
Title of host publication2016 10th International Conference on Sensing Technology, ICST 2016
ISBN (Electronic)9781509007967
Publication statusPublished - 22 Dec 2016
Externally publishedYes
Event10th International Conference on Sensing Technology, ICST 2016 - Nanjing, China
Duration: 11 Nov 201613 Nov 2016

Publication series

NameProceedings of the International Conference on Sensing Technology, ICST
ISSN (Print)2156-8065
ISSN (Electronic)2156-8073


Conference10th International Conference on Sensing Technology, ICST 2016

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