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An Approach to Design and Development Recommender System

Samir N Ajani1 , Lokesh M Heda2 , Santosh Kumar Sahu3 , Manish M Motghare4

  1. Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management , Nagpur, India.
  2. Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management , Nagpur, India.
  3. Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management , Nagpur, India.
  4. Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management , Nagpur, India.

Section:Review Paper, Product Type: Journal Paper
Volume-6 , Issue-3 , Page no. 431-433, Mar-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i3.431433

Online published on Mar 30, 2018

Copyright © Samir N Ajani, Lokesh M Heda, Santosh Kumar Sahu, Manish M Motghare . 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.

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IEEE Style Citation: Samir N Ajani, Lokesh M Heda, Santosh Kumar Sahu, Manish M Motghare, “An Approach to Design and Development Recommender System,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.431-433, 2018.

MLA Style Citation: Samir N Ajani, Lokesh M Heda, Santosh Kumar Sahu, Manish M Motghare "An Approach to Design and Development Recommender System." International Journal of Computer Sciences and Engineering 6.3 (2018): 431-433.

APA Style Citation: Samir N Ajani, Lokesh M Heda, Santosh Kumar Sahu, Manish M Motghare, (2018). An Approach to Design and Development Recommender System. International Journal of Computer Sciences and Engineering, 6(3), 431-433.

BibTex Style Citation:
@article{Ajani_2018,
author = {Samir N Ajani, Lokesh M Heda, Santosh Kumar Sahu, Manish M Motghare},
title = {An Approach to Design and Development Recommender System},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2018},
volume = {6},
Issue = {3},
month = {3},
year = {2018},
issn = {2347-2693},
pages = {431-433},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1822},
doi = {https://doi.org/10.26438/ijcse/v6i3.431433}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i3.431433}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1822
TI - An Approach to Design and Development Recommender System
T2 - International Journal of Computer Sciences and Engineering
AU - Samir N Ajani, Lokesh M Heda, Santosh Kumar Sahu, Manish M Motghare
PY - 2018
DA - 2018/03/30
PB - IJCSE, Indore, INDIA
SP - 431-433
IS - 3
VL - 6
SN - 2347-2693
ER -

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Abstract

Each day we are surrounded by any number of decisions to make. Which book should I read next? Which movie to watch? Which book to read? Which blog to follow? Or which item to buy? Finding the appropriate choice is like finding a needle in a haystack. Increasingly, we use the web and online resources to help us make a decision. As our decision making is transported and conducted in the online sphere, the use of recommendation systems has become essential in daily life. Recommendation systems have been studied and developed for more than two and a half decades. Within this period, a variety of algorithms has been developed for various application domains. The major breakthrough in development of recommender system was in 2006 when Netflix announced the $1 million to whoever improved the accuracy of his existing system called Cinematch by 10%in a machine learning and data mining competition for movie rating prediction.

Key-Words / Index Term

Recommender System, content-based, collaborative filtering, knowledge based filtering, IoT

References

[1] Belén Barragáns-Martínez, Enrique Costa-Montenegro, Jonathan Juncal-Martínez, Developing a recommender system in a consumer electronic device. Expert Systems with Applications journal 42 (2015) 4216–4228.

[2]Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems Journal, 46, 109–132.
[3]Joseph, T. R., & Jacob, S. (2014). A commerce recommender system for improving customer relationship management in shopping centers.International Journal of Engineering Trends and Technology.
[4]Ripley, B., Liu, D., Chang, M., & Kinshuk (2013). Next stop recommender. In 2013 international joint conference on awareness science and technology and ubi-media computing (iCAST-UMEDIA).
[5] M. Balabanovic and Y. Shoham, “Fab: Content-based, collaborative recommendation” Commun. ACM, vol. 40, no. 3, pp. 66–72,1997.
[6]Zhuang, X., Sun, Y., & Wei, K. (2014). Smocor: A smart mobile contact recommender based on smart phone data. In computer software and applications conference (COMPSAC), 2014 IEEE 38th annual.
[7] G. Adomavicius, A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering 17 (6) (2005) 734–749.
[8]A. Ansari, S. Essegaier, R. Kohli, Internet recommendation systems, Journal of Marketing Research 37 (3) (2000) 363–375.
[9] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniquesfor recommender systems,” IEEE Comput., vol. 42, no. 8,pp. 30–37, Aug. 2009.
[10]N. Antonopoulus, J. Salter, Cinema screen recommender agent: combining collaborative and content-based filtering, IEEE Intelligent Systems (2006) .
[11] R. Burke, “Hybrid recommender systems: Survey andexperiments,” User Model. User-Adapted Interaction, vol. 12, no. 4,pp. 331–370, 2002.
[12]O. Arazy, N. Kumar, B. Shapira, Improving Social Recommender Systems, Journal IT Professional 11 (4) (2009) 31–37.
[13]M. Balabanovic, Y. Shoham, Content-based, collaborative recommendation, Communications of the ACM 40 (3) (1997) 66–72.
[14] Y. Koren. (2009). The bellkor solution to the netflix grand prize[Online]. Available: http://www.netflixprize.com/assets/Grand
Prize2009_BPC_BellKor.pdf
[15]J. Bobadilla, A. Hernando, F. Ortega, A. Gutierrez, Collaborative filtering based on significances, Information Sciences 185 (1) (2012) 1–17.