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Reseach Article

Complex Network based Recommender System

by John Kingsley Arthur, Ronky Francis Doh, Eric Appiah Mantey, Jeremiah Osei-Kwakye
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 40
Year of Publication: 2019
Authors: John Kingsley Arthur, Ronky Francis Doh, Eric Appiah Mantey, Jeremiah Osei-Kwakye
10.5120/ijca2019919292

John Kingsley Arthur, Ronky Francis Doh, Eric Appiah Mantey, Jeremiah Osei-Kwakye . Complex Network based Recommender System. International Journal of Computer Applications. 178, 40 ( Aug 2019), 38-42. DOI=10.5120/ijca2019919292

@article{ 10.5120/ijca2019919292,
author = { John Kingsley Arthur, Ronky Francis Doh, Eric Appiah Mantey, Jeremiah Osei-Kwakye },
title = { Complex Network based Recommender System },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2019 },
volume = { 178 },
number = { 40 },
month = { Aug },
year = { 2019 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number40/30805-2019919292/ },
doi = { 10.5120/ijca2019919292 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:52:44.530040+05:30
%A John Kingsley Arthur
%A Ronky Francis Doh
%A Eric Appiah Mantey
%A Jeremiah Osei-Kwakye
%T Complex Network based Recommender System
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 40
%P 38-42
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The structural abstraction of Recommender Systems has a high resemblance as that of a Complex Network(CN). The underlining principles of the different types of Recommender Systems, such as Collaborative Filtering, Content-Based, Knowledge-Based, Utility-Based, and Hybrid are similar to the attributes and theories of Complex Networks. Considering the enormity of computation in Recommender Systems logic formulation and it's a structural similarity with Complex Networks, the research work seeks to highlight attributes of Complex Networks and Recommender Systems to suggest the applicability of theories, and principles in CN’s that can be considered in the construction of Recommender Systems. The research further elaborates on matrices for checking the correctness of the use of these theories.

References
  1. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D. U. “Complex Networks: Structure and Dynamics”, Physics Reports, Volume 424, Issues 4–5, February 2006, Pages 175-308
  2. Zanin, M., Cano1,P., Buldú, J. M., Celma, O. “Complex Networks in Recommendation Systems”, in International Conference on Computer Engineering And Applications (CEA'08) Acapulco, Mexico, January 2008pg 25-27
  3. Shah, L., Gaudani, H., Balani, P. “Survey on Recommendation System” in International Journal of Computer Applications, Vol. 137, No. 7, March 2016
  4. Nagarnaik, P., Thomas, A. “Survey on Recommendation System Methods”, in IEEE Sponsored 2nd International Conference On Electronics And Communication System, 2015
  5. Schering, A., Düffer, M., Finger, A., Bruder, I. “A Mobile Tourist Assistance and Recommendation System Based on Complex Networks” in CNIKM, ACM, November 2009.
  6. Costa, L. D., Rodrigues, F. A., Travieso, G., and Boas, P. R. V. “Characterization of complex networks: A survey of measurements”, in Advances in Physics,
Vol. 56, No. 1, February 2007, 167–242
  7. Jain, S., Grover, A., Thakur, P. S., Choudhary, S. K. “Trends, Problems And Solutions of Recommender System”, in International Conference on Computing, Communication and Automation (ICCCA2015)
  8. Kearns, M. and Rayfield, R., Squash Magazine:The Small World of Squash. 2015[online]. Available at http://squashmagazine.ussquash.com/2015/10/the-small-world-network-of-squash/. [Accessed: 7-Jan-19]
  9. Geeksforgeeks: Check whether a given graph is Bipartite or not. 2019[online]. Available at https://www.geeksforgeeks.org/bipartite-graph/
  10. Damianos Gavalas, Charalampos Konstantopoulos, Konstantinos Mastakas , and Grammati Pantziou.2014. Mobile recommender systems in tourism. Journal of Networks and Computer Applications. 9, (2014) 319–333 pages. http://dx.doi.org/10.1016/j.jnca.2013.04.006
  11. P. Brusilovsky, A. Kobsa, and W. Nejdl (Eds.): The Adaptive Web, LNCS 4321, pp. 377 – 408, 2007. Springer-Verlag Berlin Heidelberg 2007
  12. Vairachilai, S., Kavitha, M. K., and Raja, M.2017. Analysis of Statistical and Structural Properties of Complex networks with Random Networks. Applied Mathematics and Information Sciences 11, No. 1, pages 137-146
Index Terms

Computer Science
Information Sciences

Keywords

Complex Networks Recommender Systems Collaborative Filtering Content-Based RS Hybrid Recommender Systems