›› 2015, Vol. 30 ›› Issue (6): 1188-1200.doi: 10.1007/s11390-015-1593-3

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Networking and Distributed Computing for Big Data • Previous Articles     Next Articles

From Interest to Location: Neighbor-Based Friend Recommendation in Social Media

Jin-Qi Zhu1(朱金奇), Li Lu2(鲁力), Member, CCF, ACM, IEEE, Chun-Mei Ma2(马春梅)   

  1. 1 School of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China;
    2 School of Computer Science and Engineering, University of Electronic Science and Technology of China Chengdu 611731, China
  • Received:2015-05-18 Revised:2015-08-22 Online:2015-11-05 Published:2015-11-05
  • About author:Jin-Qi Zhu received her Ph.D. degree in computer science from University of Electronic Science and Technology of China, Chengdu, in 2009. She is currently an associate professor in the School of Computer and Information Engineering at Tianjin Normal University, Tianjin. Her research interests include parallel and distributed computing, mobile and wireless computing, wireless sensor networks, and vehicular ad hoc networks.
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61103227, 61172185, 61272526, 61472068, and 61173171, the China Postdoctoral Science Foundation under Grant No. 2014M550466, the Introduced Research Funds for Tianjin Normal University under Grant No. 5RL133, and the Tianjin Research Program of Application Foundation and Advanced Technology under Grant No. 15JCQNJC01400.

Recent years have witnessed the tremendous development of social media, which attracts a vast number of Internet users. The tweets these users posted provide an effective way of understanding user behaviors. A large amount of previous work benefits from mining user interest to make friend recommendation. However, the potentially strong but inconspicuous relation between location and interest interaction among social media users is overlooked in these studies. Different from the previous researches, we propose a new concept named neighbor-based friend recommendation (NBFR) to improve the friend recommendation results. By recommending surrounding users who have similar interest to each other, social media users are provided a unique opportunity to interact with surrounding people they may want to know. Based on this concept, we first mine users' interest from short tweets, and then propose to model the user interest with multiple topics under the hypercube structure for friend recommendation. At the same time, we also offer a topic matching shortcut algorithm for more extensive recommendation. The evaluations using the data gathered from the real users demonstrate the advantage of NBFR compared with the traditional recommendation approaches.

[1] Zhao W J, Jiang J, Weng J et al. Comparing Twitter and traditional media using topic models. In Proc. the 33rd ECIR, April 2011, pp.338-349.

[2] Chen Y, Zhao J C, Hu X et al. From interest to function: Location estimation in social media. In Proc. the 27th AAAI Conference on Artificial Intelligence, July 2013, pp.180- 186.

[3] Moricz M, Dosbayev Y, Berlyant M. PYMK: Friend recommendation at MySpace. In Proc. the 2010 ACM SIGMOD Int. Conf. Management of Data, June 2010, pp.999-1002.

[4] Kazienko P, Musial K, Kajdanowicz T. Multidimensional social network in the social recommender system. IEEE Trans. System, Man and Cybernetics, Part A: Systems and Humans, 2011, 41(4): 746-759.

[5] Deng Z W, He B W, Yu C C, Chen Y X. Personalized friend recommendation in social network based on clustering method. In Proc. the 6th ISICA, Oct. 2012, pp.84-91.

[6] Hannon J, Bennett M, Smyth B. Recommending Twitter users to follow using content and collaborative filtering approaches. In Proc. the 4th ACM Int. Conf. Recommender Systems, September 2010, pp.199-206.

[7] Zuo X, Chin A, Fan X G et al. Connecting people at a conference: A study of influence between offline and online using a mobile social application. In Proc. Green- Com/iThings/CPSCOM, Nov. 2012, pp.277-284.

[8] Mcpherson M, Smith-Lovin L, Cook J. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 2001, 27: 415-444.

[9] Chen J, Geyer W, Dugan C, Muller M. Make new friends, but keep the old: Recommending people on social networking sites. In Proc. the 27th Int. Conf. Human Factors in Computing Systems, April 2009, pp.201-210.

[10] HsuWH, King A L, Paradesi MS R et al. Collaborative and structural recommendation of friends using weblog-based social network analysis. In Proc. AAAI Conference on Computational Approaches to Analyzing Weblogs, March 2006, pp.55-60.

[11] Wen Y G, Zhu X Q, Rodrigues J P C, Chen C W. Cloud mobile media: Reflections and outlook. IEEE Trans. Multimedia (TMM), 2014, 16(4): 885-902.

[12] Hu H, Wen Y G, Chua T S, Li X L. Towards scalable systems for big data analytics: A technology tutorial. IEEE Access Journal, 2014, 2: 652-687.

[13] Xia W F, Wen Y G, Foh C H et al. A survey on softwaredefined networking. IEEE Commun. Surveys and Tutorials, 2015, 17(1): 27-51.

[14] Hu H,Wen Y G, Chua T S et al. Community-based effective social video contents placement in cloud-centric CDN network. In Proc. the IEEE Int. Conf. Multimedia and Expo (ICME), July 2014.

[15] Wang B D, Wang C, Bu J et al. Whom to mention: Expend the diffusion of tweets by @ recommendation on micro-blogging systems. In Proc. the 22nd ACM Int. Conf. WWW, May 2013, pp.1331-1340.

[16] Liben-Nowell D, Kleinberg J. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 2007, 58(7): 1019-1031.

[17] Zhang L Z, Fang H, Ng W K, Zhang J. IntRank: Interaction ranking-based trustworthy friend recommendation. In Proc. the 10th Int. Conf. IEEE TrustCom, Nov. 2011, pp.266-273.

[18] Wu J. Distributed System Design (1st edition). CRC Press, 1998.

[19] Huo H W, Shen W, Xu Y Z, Zhang H K. Virtual hypercube routing in wireless sensor networks for health care systems. In Proc. the 1st IEEE ICFIN, Oct. 2009, pp.178-183.

[20] Chang C Y, Chang C Y, Sheu J P. BlueCube: Constructing a hypercube parallel computing and communication environment over Bluetooth radio systems. Journal of Parallel and Distributed Computing, 2006, 66(10): 1243-1258.

[21] Wu J, Wang Y. Hypercube-based multi-path social feature routing in human contact networks. IEEE Trans. Computers, 2014, 63(2): 383-396.

[22] Fuller H Q, Fuller R M, Fuller R G. Physics, Including Human Applications (1st edition). Longman Higher Education Press, 1978.

[23] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993-1022.

[24] Wang X Y, Sun L F, Wang Z, Meng D. Group recommendation using external followee for social TV. In Proc. the 2012 IEEE ICME, July 2012, pp.37-42.

[25] Chen J T, She J. An analysis of verifications in microblogging social networks — Sina Weibo. In Proc. the 32nd ICDCSW, June 2012, pp.147-154.

[26] Fire M, Tenenboim L, Lesser O et al. Link prediction in social networks using computationally efficient topological features. In Proc. the 3rd PASSAT, Oct. 2011, pp.73-80.

[27] Zhang S K, Jiang H, Carroll J M. Integrating online and offline community through Facebook. In Proc. the 2011 IEEE Int. Conf. Collaboration Technologies and Systems, May 2011, pp.569-578.

[28] Hsu W J, Spyropoulos T, Psounis K, Helmy A. Modeling time-variant user mobility in wireless mobile networks. In Proc. the 26th IEEE INFOCOM, May 2007, pp.758-766.

[29] González M, Hidalgo C, Barabási A. Understanding individual human mobility patterns. Nature, 2008, 453(7196): 779-782.
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[1] Liu Mingye; Hong Enyu;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[2] Chen Shihua;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[3] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[4] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[5] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[6] Min Yinghua; Han Zhide;. A Built-in Test Pattern Generator[J]. , 1986, 1(4): 62 -74 .
[7] Tang Tonggao; Zhao Zhaokeng;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[8] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[9] Zhu Hong;. Some Mathematical Properties of the Functional Programming Language FP[J]. , 1987, 2(3): 202 -216 .
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