Social network document ranking

In search engines, ranking algorithms measure the importance and relevance of documents mainly based on the contents and relationships between documents. User attributes are usually not considered in ranking. This user-neutral approach, however, may not meet the diverse interests of users, who may demand different documents even with the same queries. To satisfy this need for more personalized ranking, we propose a ranking framework. Social Network Document Rank (SNDocRank), that considers both document contents and the relationship between a searched and document owners in a social network. This method combined the traditional tf-idf ranking for document contents with out Multi-level Actor Similarity (MAS) algorithm to measure to what extent document owners and the searcher are structurally similar in a social network. We implemented our ranking method in simulated video social network based on data extracted from YouTube and tested its effectiveness on video search. The results show that compared with the traditional ranking method like tf-idfs the SNDocRank algorithm returns more relevant documents. More specifically, a searcher can get significantly better results be being in a larger social network, having more friends, and being associated with larger local communities in a social network.

[1]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[2]  H. White,et al.  STRUCTURAL EQUIVALENCE OF INDIVIDUALS IN SOCIAL NETWORKS , 1977 .

[3]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[4]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[5]  Don Coppersmith,et al.  Matrix multiplication via arithmetic progressions , 1987, STOC.

[6]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[7]  M. KleinbergJon.  Authoritative sources in a hyperlinked environment , 1999 .

[8]  Jaana Kekäläinen,et al.  IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.

[9]  Stephen E. Robertson,et al.  A probabilistic model of information retrieval: development and comparative experiments - Part 2 , 2000, Inf. Process. Manag..

[10]  Stephen E. Robertson,et al.  Effective site finding using link anchor information , 2001, SIGIR '01.

[11]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[12]  David M. Pennock,et al.  Methods and metrics for cold-start recommendations , 2002, SIGIR '02.

[13]  Luca Pretto,et al.  A Theoretical Analysis of Google's PageRank , 2002, SPIRE.

[14]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[15]  Jennifer Widom,et al.  Scaling personalized web search , 2003, WWW '03.

[16]  César A. Hidalgo,et al.  Scale-free networks , 2008, Scholarpedia.

[17]  Taher H. Haveliwala.  Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search , 2003, IEEE Trans. Knowl. Data Eng..

[18]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Josep Lluís de la Rosa i Esteva,et al.  A Taxonomy of Recommender Agents on the Internet , 2003, Artificial Intelligence Review.

[20]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Huan Liu,et al.  CubeSVD: a novel approach to personalized Web search , 2005, WWW '05.

[22]  Wolfgang Nejdl,et al.  Using ODP metadata to personalize search , 2005, SIGIR '05.

[23]  Shuming Shi,et al.  Title extraction from bodies of HTML documents and its application to web page retrieval , 2005, SIGIR '05.

[24]  ChengXiang Zhai,et al.  UCAIR: a personalized search toolbar , 2005, SIGIR '05.

[25]  ChengXiang Zhai,et al.  UCAIR : Capturing and Exploiting Context for Personalized Search , 2005 .

[26]  M. Newman,et al.  Vertex similarity in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Andreas Hotho,et al.  Information Retrieval in Folksonomies: Search and Ranking , 2006, ESWC.

[28]  Krishna P. Gummadi,et al.  Exploiting Social Networks for Internet Search , 2006, HotNets.

[29]  Fabio Gasparetti,et al.  Personalized Search on the World Wide Web , 2007, The Adaptive Web.

[30]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[31]  Yong Yu,et al.  Optimizing web search using social annotations , 2007, WWW '07.

[32]  Mukesh Dalal.  Personalized social & real-time collaborative search , 2007, WWW '07.

[33]  Gerhard Weikum,et al.  Efficient top-k querying over social-tagging networks , 2008, SIGIR '08.

[34]  Xiaolong Zhang,et al.  MobiSNA: a mobile video social network application , 2009, MobiDE.

[35]  Igor Perisic,et al.  Mapping search relevance to social networks , 2009, SNA-KDD '09.

[36]  Xiaolong Zhang,et al.  SNDocRank: a social network-based video search ranking framework , 2010, MIR '10.