An Improved Cohesion Based Community Recommendation System

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dc.contributor.authorAhmed, Kazi Wasif
dc.contributor.authorRashid, Md. Mamunur
dc.date.accessioned2021-09-13T09:02:45Z
dc.date.available2021-09-13T09:02:45Z
dc.date.issued2014-11-15
dc.descriptionSupervised by Md. Kamrul Hasan, PhD Department of Computer Science & Engineering (CSE), Islamic University of Technology (IUT),en_US
dc.description.abstractSocial Networking Sites (SNS) are the dominating entities in the modern web. The importance of social networking sites in our life is increasing day by day as they are attracting millions of users by their interesting features and activities. It enables the researchers to use the information available in these sites. Online Community is appealing to people as they can enjoy sharing their ideas, view, and know about view of other people. At the same time, they are interested in joining different community. However, with the rapid growth of SNS’s resulting in information overload people are in dilemmas to choose right community from huge list of available communities and it is also time consuming. Potential choice of communities is influenced by many factors of user behavior and activeness in Social Networking Sites. The recent surge of research in recommendation algorithms is not surprising. But these algorithms have unsatisfactory results in community recommendation because of lack of intuition in judging rational behavior. Many researches are going on this point to find out recommendation system in various ways. To solve this problem, we introduce cohesion based community recommendation system. In this paper we design a general framework of community recommendation based on cohesion after analyzing the present methods of community recommendation. The main idea of the proposed approach is consisted of following stages- measuring friendship factor, measuring user factor, calculating threshold from present communities of user, community recommendation based on threshold, result analysis. We validated our idea on a small network in Facebook.en_US
dc.identifier.citation[1] Md. Nafiz Hamid, Md. Abu Naser, Md. Kamrul Hasan and Hasan Mahmud, A Cohesion Based Friend Recommendation System, Springer Journal, Social networking and Mining, February 2014. [2] Sanam Kadge and Gresha Bhatia, Graph Based Forecasting for Social Networking Site, 2012 International Conference on Communication, Information & Computing Technology (ICCICT), Oct. 19-20, Mumbai, India. [3] Marjaneh Safaei, Merve Sahan and Mustafa Ilkan, Social Graph Generation & Forecasting using Social Network Mining, 2009 33rd Annual IEEE International Computer Software and Applications Conference. [4] Nitai B. Silva, Ing-Ren Tsang, George D.C. Cavalcanti, and Ing-Jyh Tsang, A Graph-Based Friend Recommendation System Using Genetic Algorithm, WCCI 2010 IEEE World Congress on Computational Intelligence July, 18-23, 2010 - CCIB, Barcelona, Spain. [5] Jaewon Yang, Julian McAuley and Jure Leskovec, Community Detection in Networks with Node Attributes, Stanford University, Data Mining (ICDM), 2013 IEEE 13th International Conference. [6] David Crandall, Dan Cosley, Daniel Huttenlocher, Jon Kleinberg, Siddharth Suri, Feedback Effects between Similarity and Social Influence in Online Communities, KDD '08 Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM New York, NY, USA ©2008. 29 | P a g e [7] Wellman, B., Boase, J., Chen, W.: The networked nature of community. IT&Society,vol. 1(1), pp. 151- 165 (2002). [8] Gediminas Adomavicius and Alexander Tuzhilin, Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, Journal IEEE Transactions on Knowledge and Data Engineering archive, Volume 17 Issue 6, June 2005 [9] Krulwich, B., and Burkey, C., Learning user information interests through hextraction of semantically significant phrases,In Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access, Stanford, Calif., March 1996. [10] Lang, K ―Newsweeder: Learning to filter netnews, In Proceedings of the 12th International Conference on Machine Learning, Tahoe City, Calif., USA, 1995. [11] Breese, J. S., Heckerman, D., and Kadie, C, Empirical Analysis of Predictive Algorithms for Collaborative Filtering‖ in Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998, pp. 43-52. [12] Melville, Prem Mooney, Raymond J Nagarajan, Ramadass,Content-Boosted Collaborative Filtering for Improved Recommendations,Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002), pp. 187-192, Edmonton, Canada, July 2002. [13] Chin, A. and Chignell, M.,Automatic detection of cohesive subgroups within social hypertext: A heuristic approach., New Review of Hypermedia and Multimedia, Vol. 14, No. 1, July 2008, 121-143. [14] Jessica Marie Vitak, BA, Facebook “Friends”: How online identities impact offline relationships, A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Arts in Communication, Culture and Technology, April 22, 2008. [15] Group Recommendation system for Facebook, Enkh-Amgalan Baatarjav, Jedsada Chartree, and Theraput Meesumrarn, Department of Computer Science and Engineering University of North Texas, Denton, Texas, 76203, USA. [16] Mike Gartrell, Xinyu Xing, Qin Lv, Aaron Beach, Richard Han, Shivakant Mishra, Karim Seada, “Enhancing Group Recommendation by Incorporating Social Relationship Interactions”, Proceedings of the 16th ACM International Conference on Supporting Group Work (GROUP) 2010. [17] Wen-Yen Chen, Dong Zhang and Edward Y. Chang, Combinational Collaborative Filtering for Personalized community Recommendation, KDD '08 Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA ©2008.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/981
dc.language.isoenen_US
dc.publisherDepartment of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladeshen_US
dc.subjectSocial networking, Community, Recommendation system, Cohesion, friendship factor, user factor, thresholden_US
dc.titleAn Improved Cohesion Based Community Recommendation Systemen_US
dc.typeThesisen_US

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