Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Interest based Recommender System for Social Media

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Shivangi Garg

Shivangi Garg. Interest based Recommender System for Social Media. International Journal of Computer Applications 156(5):29-35, December 2016. BibTeX

	author = {Shivangi Garg},
	title = {Interest based Recommender System for Social Media},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {156},
	number = {5},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {29-35},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2016912444},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In great popularity of Social Media, flourishing massive world of online social communities entrust users to formulate themselves over set of the interests. Users love to know about the people they like and the different things they are interested into. Thus, they require most appropriate source of information sharing and alert gathering. Recommender system is a solution for it. Every site does have a recommender system but these recommender systems are mostly based on the subscription choices of user’s friends. When a user searches for a particular interest over these sites, the first few results that come up to the screen are the broader interest groups which become over crowded over the time and the user remains with the choice to join an interest group which is of lesser interest than the appropriate one. Hence, this paper proposes a interest based Recommender System named as ‘MAAC’ Recommender System, which recommends the users to follow the more specific interest feeds, solely based on the current subscribed interests of a user instead of demographic knowledge or Friends knowledge. Also, it recommends the interests which are closely related to the interests already linked with the user’s profile. The proposed recommender system proves to be highly effective in recommending the various interests to the user, thus incorporating the high percent accuracy in terms of recall, precision and F1 measure as compared to other recommender system.


  1. Albert R, Jeong H, Barab´asi AL. 1999. Internet: diameter of the world-wide web. Nature401:130–131 DOI 10.1038/43601.
  2. Barabasi AL, Albert R, Jeong H. 2000. Scale-free characteristics of random networks: the topologyof the world-wide web. Physica A 281:69–77 DOI 10.1016/S0378-4371(00)00018-2.
  3. Sanderson B, Rigby M. 2013. We’ve reddit, have you? What librarians can learn from a site full of memes. College & Research Libraries News 74:518-521.
  4. Pazzani, Michael J. and Daniel Billsus: Content-Based Recommendation Systems. In Brusilovsky, Peter, Alfred Kobsa and Wolfgang Nejdl (editors): The Adaptive Web, volume 4321 of lecture Notes in Computer Science, chapter 10, pages 325-341. Springer-verlag Berlin, Germany, May 2007.
  5. Schafer, J.Ben, Dan Frankowski, Jon Herlocker and Shilad Sen: Collaborative Filtering recommender Systems. In Brusilovsky, Peter, Alfred Kobsa And Wolfgang Nejdl (Editors): The Adaptive Web, Volume 4321 of Lecture Notes in Computer Science, Chapter 9, Pages 291-324. Springer-Verlag, Berlin, Germany, May 2007.
  6. Adomavicius and Tuzhilin: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEETKDE: IEEE Transactions on Knowledge and Data Engineering, 17, 2005.
  7. Herbert A. Simon. Administrative Behavior, 4th Edition, pages 155, 184, 216. Free Press, 4 sub edition, March 1997.
  8. Herbert A. Simon. The sciences of the artificial (3rd ed.), pages 5, 216. MIT Press, Cambridge, MA, USA, 1996.
  9. Olson and Neal (2015), Navigating the massive world of reddit: using backbone networks to map user interests in social media. PeerJ Comput. Sci. 1:e4; DOI 10.7717/peerj-cs.4.
  10. Huang Zan, Hsinchun Chen, and Daniel D. Zeng. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst., 22(1):116{142, January 2004.
  11. Scott J., Social Network Analysis: A Handbook. Sage Publications, London, 2nd edition (2000).
  12. Newman, M.E.J. & Girvan, M. Finding and evaluating community structure in networks. Physical Review E 69, 26113(2004).
  13. Wang Jian and Zhang Yi. Utilizing marginal net utility for recommendation in e-commerce. In Proceedings of the 34th ACM SIGIR’11, pages 1003–1012. ACM, 2011.
  14. Gang Zhao, Mong Li Lee, Wynne Hsu, and Wei Chen. Increasing temporal diversity with a. purchase intervals.
  15. In Proceedings of the 35th ACM SIGIR, pages 165–174, New York,
  16. NY, USA, 2012. ACM.
  17. Murata Tsuyoshi and Moriyasu Sakiko (2007), Link Prediction of Social Networks Based on Weighted Proximity Measures. IEEE/WIC/ACM International Conference on Web Intelligence; DOI 10.1109/WI.2007.52
  18. Adamic, L. A., Adar, E., Friends and Neighbors on the Web, Social Networks, Vol.25, No.3, pp.211-230, 2003.
  19. Ekstrand M. D., J. T. Riedl, and J. A. Konstan. Collaborative filtering recommender systems. Foundations and Trends in Human-Computer Interaction, 4(2):81{173, 2011.
  20. FigShare: behavior/874101.
  21. Jing Li, Lingling Zhang, Fan Meng, Fenhua Li (2014). Recommendation Algorithm Based On Link Prediction And Domain Knowledge In Retail Transactions. International Conference on Information Technology and Quantitative Management, ITQM; pp 875 – 881.
  22. Ma H., D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender Systems with Social Regularization. In ACM International Conference on Web Search and Data Mining (WSDM), 2011.


Social Network, Reddit, Subreddits, Recommender System, Link Prediction, Degree Centrality, Clustering