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Socially inspired search and ranking in mobile
social networking: concepts and challenges |
| Yufeng WANG1,Akihiro NAKAO2,Jianhua MA3, |
| 1.College of Telecommunications
and Information Engineering, Nanjing University of Posts and Telecommunication,
Nanjing 230003, China；National Institute
of Information and Communications Technology (NICT), Tokyo 113-0001,
Japan; 2.Graduate School
of Interdisciplinary Information Studies, University of Tokyo, Tokyo
113-8654, Japan；National Institute
of Information and Communications Technology (NICT), Tokyo 113-0001,
Japan; 3.Faculty of Computer &
Information Sciences, Hosei University, Tokyo 184-8584, Japan; |
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Abstract In this paper, we provide an overview of challenges in mobile search and ranking, and envision the fundamental features that should be satisfied. We argue that two principles will help improve the relevance and quality of mobile search and ranking: the first one is to examine both intrinsic content features and context of items (usage statistics and social features, etc.); and the second one lies in that no algorithms can replace the objectivity of a human being—let users define the sites that they feel are relevant, leverage their social networks, and over time see their results become highly personalized. Specifically, wireless-virtualcommunity- based mobile search and ranking architecture is proposed in this paper, in which communities act as a first class abstraction for information sharing. Then, we introduce briefly the potential procedures of achieving high relevance and quality in mobile search and ranking based on wireless virtual community.
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| Keywords
mobile search and ranking
social networking
virtual community
link analysis
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Issue Date: 05 December 2009
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Parameswaran M, Whinston A B. Social computing: an overview. Communications of the Association for InformationSystems, 2007, 19: 762―780
|
|
Surowiecki J. TheWisdom of Crowds. Anchor, 2005
|
|
Bjelland J, Canright G S, Kenth E M. Web link analysis: estimating a document’s importancefrom its context. Telektronikk, 2008, 95―113
|
|
Geoffrey S, Canright G S, Kenth E M. Introducing network analysis, Telektronikk, 2008, 4―18
|
|
Page L, Brin S, Motwani R, Winograd T. ThePageRank citation ranking: bringing order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998
|
|
Kleinberg J M. Authoritative sources in a hyperlinked environment. Journal of the ACM, 1999, 46(5): 604―632
doi: 10.1145/324133.324140
|
|
Lempel R, Moran S. SALSA: the stochastic approachfor linkstructure analysis. ACM Transactionsin Information Systems, 2001, 19(2): 131―160
doi: 10.1145/382979.383041
|
|
Zhang J, Ackerman M S, Adamic L. Expertise networksin online communities: structure and algorithms. In: Proceedings of the 16th WWW, 2007, 221―230
|
|
Jurczyk P, Agichtein E. Discovering authorities inquestion answer communities by using link analysis. In: Proceedings of the ACM 16th CIKM, 2007, 919―922
|
|
Ziegler C N, Lausen G. Propagation models for trustand distrust in social networks. InformationSystems Frontiers, 2005, 7(4/5): 337―358
doi: 10.1007/s10796-005-4807-3
|
|
Guha R, Kumar R, Raghavan P, et al. Propagation of trust and distrust. In: Proceedings of the 13th international conferenceon WWW, 2004, 403―412
|
|
Kamvar S D, Schlosser M T, Garcia-Molina H. The EigenTrust algorithm for reputationmanagement in P2P networks. In: Proceedingsof the 12th WWW, 2003, 640―651
|
|
Gyongyi Z, Garcia-Molina H, Pedersen J. Combating web spam with TrustRank. In: Proceedings of the 30th International Conference on VLDB, 2004, 576―587
|
|
Greenwald A, Wicks J. QuickRank: a recursive rankingalgorithm. In: Proceedings of the 1st InternationalWorkshop on Computational Social Choice, 2006, 220―233
|
|
Weinman J. Anew approach to search. Business CommunicationsReview, 2007, 19―29
|
|
Joachims T, Granka L A, Pan B, et al. Accurately interpreting clickthrough data asimplicit feedback. In: Proceedings of SIGIR, 2005, 154―161
|
|
Seada K, Perkins C. Social networks: the killerapp for wireless Ad hoc networks? TechniqueReport in Nokia. 2006
|
|
Cao Y W, Krebs M, Toubekis G, et al. Mobile community information systems on wirelessmesh networks- an opportunity for developing countries and rural areas. In: Proceedings of the 5th International Workshopon Ubiquitous Mobile Information and Collaboration Systems (UMICS), 2007, 483―497
|
|
Mislove A, Gummadi K P, Druschel P. Exploiting social networks for Internet search. In: Proceedings of the HotNets, 2006
|
|
Luxenburger J, Weikum G. Exploiting community behaviorfor enhanced link analysis and web search. In: Proceedings of the 9th International Workshop on the Web and Databases(WebDB), 2006
|
|
Teevan J, Dumais S T, Horvitz E. Personalizing search via automated analysis of interestsand activities. In: Proceedings of theSIGIR, 2005, 449―456
|
|
Teevan J, Morris M R, Bush S. Discovering and using groups to improve personalizedsearch. In: Proceedings of the 2nd ACMInternational Conference on Web Search and Data Mining (WSDM), 2009, 15―24
|
|
Agichtein E, Castillo C, Donato D, et al. Finding high-quality content in social media. In: Proceedings of the 1st ACM International Conferenceon Web Search and Data Mining (WSDM), 2008, 183―194
|
|
Bian J, Liu Y D, Agichtein E, et al. Finding the right facts in the crowd: factoidquestion answering over social media. In: Proceedings of 17th WWW, 2008, 467―476
|
|
Hillebrand C, Groh G, Koch M. Mobile communities―extending online communitiesinto the real world. Mobile and CollaborativeBusiness, 2002, 7―18
|
|
Wellman B. Community:from neighborhood to network. Communicationsof the ACM, 2005, 48(10): 53―55
doi: 10.1145/1089107.1089137
|
|
Newman M E. Finding community structure in networks using the eigenvectors ofmatrices. Physical Review E, 2006, 74(3): 036104
doi: 10.1103/PhysRevE.74.036104
|
|
Danon L, Diaz-Guilera A, Duch J, et al. Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005
|
|
Clauset A. Findinglocal community structure in networks. Physical Review E, 2005, 72(2): 026132
doi: 10.1103/PhysRevE.72.026132
|
|
Clauset A, Newman M E, Moore C. Finding community structure in very large networks. Physical Review E, 2004, 70: 066111
doi: 10.1103/PhysRevE.70.066111
|
|
Sepandar K, Taher H, Christopher M, et al. Exploiting the block structure of the web forcomputing PageRank. Technique Report. Stanford, 2003
|
|
Castano S, Montanelli S. Semantic self-formation ofcommunities of peers. In: Proceedings ofthe ESWC, 2005
|
|
Wang Y F, Wang W D, Sakurai C,et al. On studying P2P topology construction based onvirtual region and its effect on search performance. In: Proceedings of the 3rd UIC, 2006
|
|
Das T, Nandi S, Ganguly N. Community formation and search in P2P: a robust and self-adjustingalgorithm. In: Proceedings of the 3rd Workshopon IAMCOM, 2009
|
|
Kuhn M, Wattenhofer R. Community-aware mobile networking. In: Proceedings of the 1st Workshop on MSPE, 2006
|
|
Motani M, Srinivasan V, Nuggehalli P S. PeopleNet: engineering a wireless virtual social network. In: Proceedings of MobiCom, 2005
|
|
Bakos B, Farkas L, Nurminen J K. Search engine for phonebook-based smart phone networks. In: Proceedings of the IEEE 61st Semiannual VehicularTechnology Conference (VTC), 2005
|
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