Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

A Survey on Recommendation System

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Debashis Das, Laxman Sahoo, Sujoy Datta
10.5120/ijca2017913081

Debashis Das, Laxman Sahoo and Sujoy Datta. A Survey on Recommendation System. International Journal of Computer Applications 160(7):6-10, February 2017. BibTeX

@article{10.5120/ijca2017913081,
	author = {Debashis Das and Laxman Sahoo and Sujoy Datta},
	title = {A Survey on Recommendation System},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {7},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {6-10},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume160/number7/27083-2017913081},
	doi = {10.5120/ijca2017913081},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Recommendation systems have become extremely common in recent years. It helps the customer to discover information and settle on choices where they do not have the required learning to judge a specific item. It can be utilized as a part of different diverse approaches to encourage its customer with effective information sorting. It is a software tool and techniques that provide suggestion based on the customer's taste to discover new appropriate thing for them by filtering personalized information based on the user's preferences from a large volume of information. Users taste and preferences should be constructed accurately in order to provide most relevant suggestions. This survey paper compare's and details the various type of recommender system and popular recommendation algorithms and its uses.

References

  1. Website link https://en.wikipedia.org/wiki/E-commerce_in_India
  2. Website link https://www-01.ibm.com/software/data/bigdata/what-is-big-data.html
  3. TechAmerica Foundation’s Federal Big Data Commission. (2012). Demystifying big data: A practical guide to transforming the business of Government. retrieved from http://www.techamerica.org/Docs/fileManager.cfm?f=techamerica-bigdatareport-final.pdf.
  4. Schafer, J. Ben, Joseph A. Konstan, and John Riedl. "E-commerce recommendation applications." Applications of Data Mining to Electronic Commerce. Springer US, 2001. 115-153.
  5. Fernández, Alberto, et al. "An overview on the structure and applications for business intelligence and data mining in cloud computing." 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing. Springer Berlin Heidelberg, 2013.
  6. Xiang, Liang. "Recommendation system practice." BeiJing Youdian Publication House, BeiJing (2012): 6.
  7. Witten I. H. and Frank I. Data Mining, Morgan Kaufman Publishers, San Francisco, 2000.
  8. GK, Kiran Kumar Bathi, and N. Sheela. "Personalized Recommender System." PARIPEX-Indian Journal of Research 4.8 (2016).
  9. Asabere, Nana Yaw. "Review of Recommender Systems for Learners in Mobile Social/Collaborative Learning." International Journal of Information 2.5 (2012).
  10. F Ricci, L. Rokach, B. S Paul. KantorRecommender Systems Handbook”, pg.77.
  11. Núñez-Valdéz, Edward Rolando, et al. "Implicit feedback techniques on recommender systems applied to electronic books." Computers in Human Behavior 28.4 (2012): 1186-1193.
  12. Jawaheer, Gawesh, Martin Szomszor, and Patty Kostkova. "Comparison of implicit and explicit feedback from an online music recommendation service." proceedings of the 1st international workshop on information heterogeneity and fusion in recommender systems. ACM, 2010.
  13. Lops, Pasquale, Marco De Gemmis, and Giovanni Semeraro. "Content-based recommender systems: State of the art and trends." Recommender systems handbook. Springer US, 2011. 73-105.
  14. Amatriain, Xavier, Josep M. Pujol, and Nuria Oliver. "I like it... i like it not: Evaluating user ratings noise in recommender systems." International Conference on User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2009.
  15. Zhao, Zhi-Dan, and Ming-Sheng Shang. "User-based collaborative-filtering recommendation algorithms on hadoop." Knowledge Discovery and Data Mining, 2010. WKDD'10. Third International Conference on. IEEE, 2010.
  16. Chen, Yan-Ying, An-Jung Cheng, and Winston H. Hsu. "Travel recommendation by mining people attributes and travel group types from community-contributed photos." IEEE Transactions on Multimedia 15.6 (2013): 1283-1295.
  17. Jin, Yohan, et al. "MySpace video recommendation with map-reduce on qizmt." Semantic Computing (ICSC), 2010 IEEE Fourth International Conference on. IEEE, 2010.
  18. Meng, Shunmei, et al. "KASR: A Keyword-Aware Service Recommendation method on MapReduce for big data applications." IEEE Transactions on Parallel and Distributed Systems 25.12 (2014): 3221-3231.
  19. Chen, Li, and Feng Wang. "Preference-based clustering reviews for augmenting e-commerce recommendation." Knowledge-Based Systems 50 (2013): 44-59.
  20. Luo, Zhiling, Ying Li, and Jianwei Yin. "Location: a feature for service selection in the era of big data." Web Services (ICWS), 2013 IEEE 20th International Conference on. IEEE, 2013.
  21. Liu, Hongyan, et al. "Combining user preferences and user opinions for accurate recommendation." Electronic Commerce Research and Applications 12.1 (2013): 14-23.
  22. Esparza, Sandra Garcia, Michael P. O’Mahony, and Barry Smyth. "Mining the real-time web: a novel approach to product recommendation." Knowledge-Based Systems 29 (2012): 3-11

Keywords

Recommendation system, Types of the recommendation system, Feedback techniques