Routing of Queries in Social Information Retrieval Using Latent and Explicit Semantic Cues

Social Information Retrieval can be interpreted as querying the private information spaces of others within one's social network. One of the crucial steps in such a search approach is to identify the set of potential information providers to route the query to. In this experiment, we compare various routing mechanisms based on topic models (Latent Dirichlet Allocation, LDA), Explicit Semantic Analysis (ESA), and traditional metrics like Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) to identify expertise using a publicly available data collection with 1, 400 scientific abstracts including author information, queries, and relevance judgments. The abstracts are interpreted as knowledge profile in a social information retrieval scenario. Our results suggest that both LDA and ESA can solve the routing problem, whereas the LDA-based approach and a new ESA approach considering links between semantic concepts perform best on the tested dataset.

[1]  Bart Selman,et al.  Referral Web: combining social networks and collaborative filtering , 1997, CACM.

[2]  Morten T. Hansen,et al.  The Search-Transfer Problem: The Role of Weak Ties in Sharing Knowledge across Organization Subunits , 1999 .

[3]  Miles Efron,et al.  Questions are content: A taxonomy of questions in a microblogging environment , 2010, ASIST.

[4]  C. Elkan,et al.  Topic Models , 2008 .

[5]  Meredith Ringel Morris,et al.  Factors Affecting Response Quantity, Quality, and Speed for Questions Asked Via Social Network Status Messages , 2011, ICWSM.

[6]  M. de Rijke,et al.  Determining Expert Profiles (With an Application to Expert Finding) , 2007, IJCAI.

[7]  Nicholas J. Belkin,et al.  Predicting users' domain knowledge from search behaviors , 2011, SIGIR.

[8]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[9]  Krisztian Balog,et al.  A User-Oriented Model for Expert Finding , 2011, ECIR.

[10]  S. Kiesler,et al.  The kindness of strangers: on the usefulness of electronic weak ties for technical advice , 1996 .

[11]  Yuh-Jzer Joung,et al.  A Comparative Study of Expert Search Strategies in Online Social Networks , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[12]  Yiming Yang,et al.  Introducing the Enron Corpus , 2004, CEAS.

[13]  Daniel Z. Levin,et al.  The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer , 2004, Manag. Sci..

[14]  B. Kogut,et al.  Exploring internal stickiness : Impediments to the transfer of best practice within the firm , 2007 .

[15]  Evgeniy Gabrilovich,et al.  Wikipedia-based Semantic Interpretation for Natural Language Processing , 2014, J. Artif. Intell. Res..

[16]  Lynn A. Streeter,et al.  Who Knows: A System Based on Automatic Representation of Semantic Structure , 1988, RIAO Conference.

[17]  Albert Trias i Mansilla,et al.  Survey of social search from the perspectives of the village paradigm and online social networks , 2013, J. Inf. Sci..

[18]  M. de Rijke,et al.  Expertise Retrieval , 2012, Found. Trends Inf. Retr..

[19]  Grzegorz Kukla,et al.  SocLaKE: Social Latent Knowledge Explorator , 2012, Comput. J..

[20]  B. Uzzi,et al.  Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness , 1997 .

[21]  Ali Shiri,et al.  Social search: A taxonomy of, and a user-centred approach to, social web search , 2011, Program.

[22]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[23]  Leonard N. Foner,et al.  Yenta: a multi-agent, referral-based matchmaking system , 1997, AGENTS '97.

[24]  Ryen W. White,et al.  Lessons from the journey: a query log analysis of within-session learning , 2014, WSDM.

[25]  David R. Karger,et al.  Tie strength in question & answer on social network sites , 2012, CSCW '12.

[26]  Cliff Lampe,et al.  Help is on the way: patterns of responses to resource requests on facebook , 2014, CSCW.

[27]  Dominik Endres,et al.  A new metric for probability distributions , 2003, IEEE Transactions on Information Theory.

[28]  Damon Horowitz,et al.  The anatomy of a large-scale social search engine , 2010, WWW '10.

[29]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[30]  Stephen J. Payne,et al.  Knowledge in the head and on the web: using topic expertise to aid search , 2008, CHI.

[31]  Mark S. Granovetter.  T H E S T R E N G T H O F WEAK TIES: A NETWORK THEORY REVISITED , 1983 .

[32]  Djoerd Hiemstra,et al.  Modeling Documents as Mixtures of Persons for Expert Finding , 2008, ECIR.

[33]  Irma Becerra-Fernandez,et al.  Searching for experts on the Web: A review of contemporary expertise locator systems , 2006, TOIT.

[34]  Xin Li,et al.  User Profiling in the Chronobot/Virtual Classroom System , 2007, Int. J. Softw. Eng. Knowl. Eng..

[35]  Rob Cross,et al.  A Relational View of Information Seeking and Learning in Social Networks , 2003, Manag. Sci..

[36]  Mark S. Ackerman,et al.  Searching for expertise in social networks: a simulation of potential strategies , 2005, GROUP.

[37]  Meredith Ringel Morris,et al.  To search or to ask: the routing of information needs between traditional search engines and social networks , 2014, CSCW.

[38]  Theodoros Lappas,et al.  A Survey of Algorithms and Systems for Expert Location in Social Networks , 2011, Social Network Data Analytics.

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

[40]  Dawit Yimam,et al.  Expert Finding Systems for Organizations: Domain Analysis and The DEMOIR Approach , 1999 .