A Hybrid Approach for Performing Accurate prediction of Green Products using Recommender System
Jatinder Kaur1 , Rajeev Kumar Bedi2 , S.K. Gupta3
Section:Research Paper, Product Type: Journal Paper
Volume-6 ,
Issue-10 , Page no. 136-139, Oct-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i10.136139
Online published on Oct 31, 2018
Copyright © Jatinder Kaur, Rajeev Kumar Bedi, S.K. Gupta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
View this paper at Google Scholar | DPI Digital Library
How to Cite this Paper
- IEEE Citation
- MLA Citation
- APA Citation
- BibTex Citation
- RIS Citation
IEEE Style Citation: Jatinder Kaur, Rajeev Kumar Bedi, S.K. Gupta, “A Hybrid Approach for Performing Accurate prediction of Green Products using Recommender System,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.136-139, 2018.
MLA Style Citation: Jatinder Kaur, Rajeev Kumar Bedi, S.K. Gupta "A Hybrid Approach for Performing Accurate prediction of Green Products using Recommender System." International Journal of Computer Sciences and Engineering 6.10 (2018): 136-139.
APA Style Citation: Jatinder Kaur, Rajeev Kumar Bedi, S.K. Gupta, (2018). A Hybrid Approach for Performing Accurate prediction of Green Products using Recommender System. International Journal of Computer Sciences and Engineering, 6(10), 136-139.
BibTex Style Citation:
@article{Kaur_2018,
author = {Jatinder Kaur, Rajeev Kumar Bedi, S.K. Gupta},
title = {A Hybrid Approach for Performing Accurate prediction of Green Products using Recommender System},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2018},
volume = {6},
Issue = {10},
month = {10},
year = {2018},
issn = {2347-2693},
pages = {136-139},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2993},
doi = {https://doi.org/10.26438/ijcse/v6i10.136139}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i10.136139}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2993
TI - A Hybrid Approach for Performing Accurate prediction of Green Products using Recommender System
T2 - International Journal of Computer Sciences and Engineering
AU - Jatinder Kaur, Rajeev Kumar Bedi, S.K. Gupta
PY - 2018
DA - 2018/10/31
PB - IJCSE, Indore, INDIA
SP - 136-139
IS - 10
VL - 6
SN - 2347-2693
ER -
VIEWS | XML | |
603 | 279 downloads | 269 downloads |
Abstract
Today many distinct products exists along with the configuration. Technology is advancing as well, proposed system deals with recommender system based on KNN clustering techniques. KNN along with filtering mechanism is introduced as a base mechanism to predict most likely products to be promoted through the recommender system. Simulation results indicates that the C-KNN (Content based K nearest neighbour technique is better than individual approaches of KNN and content based filtering.
Key-Words / Index Term
Configuration, Recommender system, KNN, C-KNN
References
[1] Jabbar MA, Deekshatulu BL, Chandra P. Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm. Procedia Technol [Internet]. Elsevier B.V.; 2013;10:85–94. Available from: http://dx.doi.org/10.1016/j.protcy.2013.12.340
http://www.sciencedirect.com/science/article/pii/S2212017313004945
[2] Enriko IKA, Suryanegara M, Gunawan D. Heart Disease Prediction System using k-Nearest Neighbor Algorithm with Simplified Patient ’ s Health Parameters. 1843;8(12).
[3] Berka, T., & Plößnig M. Designing Recommender Systems for Tourism. Proc ENTER 2004. 2004;
[4] Wanaskar UH, Vij SR, Mukhopadhyay D. A Hybrid Web Recommendation System Based on the Improved Association Rule Mining Algorithm. J Softw Eng Appl [Internet]. 2013;2013(August):396–404. Available from: http://www.scirp.org/journal/PaperInformation.aspx?paperID=35243#.U1XQhF5YzWo
[5] Berkovsky S, Freyne J. Web Personalization and Recommender Systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’15 [Internet]. New York, New York, USA: ACM Press; 2015 [cited 2016 Feb 18]. p. 2307–8. Available from: http://dl.acm.org/citation.cfm?id=2783258.2789995
[6] Bourke S. The Application of Recommender Systems in a Multi Site, Multi Domain Environment. In: Proceedings of the 9th ACM Conference on Recommender Systems - RecSys ’15 [Internet]. New York, New York, USA: ACM Press; 2015 [cited 2016 Feb 18]. p. 229–229. Available from: http://dl.acm.org/citation.cfm?id=2792838.2799495
[7] Choi IY, Kim JK, Ryu YU. A Two-Tiered Recommender System for Tourism Product Recommendations. In: 2015 48th Hawaii International Conference on System Sciences [Internet]. IEEE; 2015 [cited 2016 Feb 18]. p. 3354–63. Available from: http://dl.acm.org/citation.cfm?id=2760444.2761472
[8] General Chair-Bergman L, General Chair-Tuzhilin A, Program Chair-Burke R, Program Chair-Felfernig A, Program Chair-Schmidt-Thieme L. Proceedings of the third ACM conference on Recommender systems. In: Proceedings of the third ACM conference on Recommender systems [Internet]. ACM; 2009 [cited 2016 Feb 18]. Available from: http://dl.acm.org/citation.cfm?id=1639714
[9] Hong H-K, Park K-W, Lee D-H. Tag recommendation system for multimedia retrieval in mobile Environment. In: The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014) [Internet]. IEEE; 2014 [cited 2016 Feb 18]. p. 1–2. Available from: http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6884307
[10] Lee Y-L, Huang F-H. Recommender system architecture for adaptive green marketing. Expert Syst Appl [Internet]. Pergamon Press, Inc.; 2011 Aug 1 [cited 2016 Feb 18];38(8):9696–703. Available from: http://dl.acm.org/citation.cfm?id=1967763.1968016
[11] Schafer J Ben, Konstan J, Riedi J. Recommender systems in e-commerce. In: Proceedings of the 1st ACM conference on Electronic commerce - EC ’99 [Internet]. New York, New York, USA: ACM Press; 1999 [cited 2015 Dec 2]. p. 158–66. Available from: http://dl.acm.org/citation.cfm?id=336992.337035
[12] Baltrunas L. Context-Aware Collaborative Filtering Recommender Systems. 2011;4(April):172.
[13] Resnick P, Varian H. Recommender systems. Commun ACM [Internet]. 1997;1–21. Available from: http://dl.acm.org/citation.cfm?id=245121