Friend Recommendation System for the Social Network based on User Behavior

Hasan, Mohammed Mehedi; Shaon, Noor Hussain

dc.contributor.author	Hasan, Mohammed Mehedi
dc.contributor.author	Shaon, Noor Hussain
dc.date.accessioned	2021-09-13T09:33:59Z
dc.date.available	2021-09-13T09:33:59Z
dc.date.issued	2014-11-15
dc.identifier.citation	[1] Boyd, Danah; Ellison, Nicole, "Social Network Sites: Definition, History, and Scholarship", Journal of Computer- Mediated Communication, Vol.13, No.1, 2007 [2] Catanese, Salvatore Meo, Pasquale De Ferrara, Emilio Fiumara, Giacomo. ”Analyzing the Facebook Friendship Graph.” [3] Gediminas Adomavicius and Alexander Tuzhilin. “Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions.” [4] 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. [5] Lang, K., “Newsweeder: Learning to filter netnews,” In Proceedings of the 12th International Conference on Machine Learning, Tahoe City, Calif., USA, 1995. [6] Herlocker, J. L., Konstan, J. A., and Riedl, J, "Explaining Collaborative Filtering Recommendations," In Proceeding of ACM 2000 Conference on Computer Supported Cooperative Work, 2000. [7] 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. [8] Long Jin et al (2013), “Understanding User Behavior in Online Social Networks: A Survey” [9] C. Wilson et al (2009), “User Interactions in Social Networks and Their Implications,” [10] Marcelo Maia et al, “Identifying User Behavior in Online Social Networks” [11] Xing Xie (2010), “Potential Friend Recommendation in Online Social Network” Page \| 22 [12] Francis T. O’Donovan et al (2013), “Characterizing user behavior and information propagation on a social multimedia network” [13] Md. Nafiz Hamid (2014), “A cohesion-based friend-recommendation system” [14] Micheal Moricz et al (2010), “PYMK: friend recommendation at myspace” [15] Hajime Hotta et al (2007), “User Profiling System Using Social Networks for Recommendation” [16] Zhiwei Deng et al (2012), “Personalized friend recommendation in social network based on clustering method” [17] Yu Zheng, Yukun Chen, Xing Xie, Wei-Ying Ma “GeoLife2.0: A Location-Based Social Networking Service‖.”	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/988
dc.description	Supervised by Md. Kamrul Hasan, Phd, Assistant Professor, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh.	en_US
dc.description.abstract	Social network sites have connected millions of users creating the social revolution in Web 2.0 now-a-days. If the group of people or organizations have the common interest then, a social network is constituted. In the present world, the most visited sites in the Internet are Twitter, Facebook, Orkut, Google plus etc. which is actually Online social networking sites. In the social network sites, a user makes friends with the other users and enjoy the communication with them. However, the large amount of online users and their diverse and dynamic interests possess great challenges to support such a novel feature in online social networks. In this thesis, we design a general friend recommendation framework based on user behavior. The main idea of the proposed method is consisted of the following stages- measuring the frequency of the activities and updating the dataset according to the activities, applying FP-Growth algorithm to find out the user behavior, then finding out the uncommon behavior containing the common behavior.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Friend Recommendation System for the Social Network based on User Behavior	en_US
dc.type	Thesis	en_US