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

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

Abstract

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.

LanguageEnglish
Title of host publicationICST 2016
Subtitle of host publication10th International Conference on Sensing Technology : proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (Electronic)9781509007967
DOIs
Publication statusPublished - 22 Dec 2016
Externally publishedYes
Event10th International Conference on Sensing Technology, ICST 2016 - Nanjing, China
Duration: 11 Nov 201613 Nov 2016

Other

Other10th International Conference on Sensing Technology, ICST 2016
CountryChina
CityNanjing
Period11/11/1613/11/16

Fingerprint

Recommender systems
Electronic commerce
Collaborative filtering
Clustering algorithms
Experiments

Keywords

  • collaborative based method
  • content based method
  • Eclat algorithm
  • hybrid based method
  • K-means clustering

Cite this

Pandya, S., Shah, J., Joshi, N., Ghayvat, H., Mukhopadhyay, S. C., & Yap, M. H. (2016). A novel hybrid based recommendation system based on clustering and association mining. In ICST 2016: 10th International Conference on Sensing Technology : proceedings (pp. 1-6). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICSensT.2016.7796287
Pandya, S. ; Shah, J. ; Joshi, N. ; Ghayvat, H. ; Mukhopadhyay, S. C. ; Yap, M. H. / A novel hybrid based recommendation system based on clustering and association mining. ICST 2016: 10th International Conference on Sensing Technology : proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2016. pp. 1-6
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Pandya, S, Shah, J, Joshi, N, Ghayvat, H, Mukhopadhyay, SC & Yap, MH 2016, A novel hybrid based recommendation system based on clustering and association mining. in ICST 2016: 10th International Conference on Sensing Technology : proceedings. Institute of Electrical and Electronics Engineers (IEEE), pp. 1-6, 10th International Conference on Sensing Technology, ICST 2016, Nanjing, China, 11/11/16. https://doi.org/10.1109/ICSensT.2016.7796287

A novel hybrid based recommendation system based on clustering and association mining. / Pandya, S.; Shah, J.; Joshi, N.; Ghayvat, H.; Mukhopadhyay, S. C.; Yap, M. H.

ICST 2016: 10th International Conference on Sensing Technology : proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 1-6.

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

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Pandya S, Shah J, Joshi N, Ghayvat H, Mukhopadhyay SC, Yap MH. A novel hybrid based recommendation system based on clustering and association mining. In ICST 2016: 10th International Conference on Sensing Technology : proceedings. Institute of Electrical and Electronics Engineers (IEEE). 2016. p. 1-6 https://doi.org/10.1109/ICSensT.2016.7796287