Making Mainstream Web Search Engines More Collaborative

Web search engines are perhaps the killer app of the modern internet age by providing users with near instant access to the world’s information. The success of modern web search engines is due in large part to the ability to handle web-scale information retrieval and also by the sophistication of their algorithmic ranking systems, which combine a variety of measures in order to determine page relevance when it comes to a specific search query. And by and large the heart of web search has remained stable over the past 10 years. However, today researchers are exploring a new approach to supporting web search, once the complements algorithmic ranking techniques by harnessing social signals and supporting a more collaborative view of web search. In this paper we motivate and review recent work in this line of research, including an in-depth case-study of the HeyStaks collaborative search platform.

[1]  Christos Makris,et al.  Category ranking for personalized search , 2007, Data Knowl. Eng..

[2]  Peter Pirolli,et al.  Do your friends make you smarter?: An analysis of social strategies in online information seeking , 2010, Inf. Process. Manag..

[3]  Alexander Pretschner,et al.  Ontology based personalized search , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.

[4]  Madhu C. Reddy,et al.  Collaborative information seeking: A field study of a multidisciplinary patient care team , 2008, Inf. Process. Manag..

[5]  Philippe A. Palanque,et al.  Human-Computer Interaction – INTERACT 2007 , 2007, Lecture Notes in Computer Science.

[6]  Bernard J. Jansen,et al.  A model for understanding collaborative information behavior in context: A study of two healthcare teams , 2008, Inf. Process. Manag..

[7]  Jon Kleinberg,et al.  Authoritative sources in a hyperlinked environment , 1999, SODA '98.

[8]  Barry Smyth,et al.  Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine , 2004, User Modeling and User-Adapted Interaction.

[9]  Barry Smyth,et al.  A Live-User Evaluation of Collaborative Web Search , 2005, IJCAI.

[10]  Chirag Shah,et al.  Algorithmic mediation for collaborative exploratory search , 2008, SIGIR '08.

[11]  Barry Smyth,et al.  A Case-Based Perspective on Social Web Search , 2009, ICCBR.

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

[13]  Francesco Ricci,et al.  User Modeling, Adaptation, and Personalization , 2013, Lecture Notes in Computer Science.

[14]  David Konopnicki,et al.  Queries as anchors: selection by association , 2005, HYPERTEXT '05.

[15]  Thorsten Joachims,et al.  Eye-tracking analysis of user behavior in WWW search , 2004, SIGIR '04.

[16]  Eric Horvitz,et al.  SearchTogether: an interface for collaborative web search , 2007, UIST.

[17]  Eric Horvitz,et al.  S3: Storable, Shareable Search , 2007, INTERACT.

[18]  Carlo Strapparava,et al.  Adaptive Hypermedia and Adaptive Web-Based Systems, 5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008. Proceedings , 2008, AH.

[19]  Meredith Ringel Morris,et al.  A survey of collaborative web search practices , 2008, CHI.

[20]  ChengXiang Zhai,et al.  Implicit user modeling for personalized search , 2005, CIKM '05.

[21]  Madhu C. Reddy,et al.  A finger on the pulse: temporal rhythms and information seeking in medical work , 2002, CSCW '02.

[22]  Alan F. Smeaton,et al.  iBingo mobile collaborative search , 2008, CIVR '08.

[23]  Yong Yu,et al.  Identifying ambiguous queries in web search , 2007, WWW '07.

[24]  Luc Lamontagne,et al.  Case-Based Reasoning Research and Development , 1997, Lecture Notes in Computer Science.

[25]  Madhu C. Reddy,et al.  Coordinating heterogeneous work: Information and representation in medical care , 2001, ECSCW.

[26]  Antonio Gulli,et al.  The indexable web is more than 11.5 billion pages , 2005, WWW '05.

[27]  Olivia R. Liu Sheng,et al.  Interest-based personalized search , 2007, TOIS.

[28]  Amanda Spink,et al.  A Study of Web Search Trends , 2004, Webology.

[29]  Barry Smyth.  The sensor web : bringing information to life , 2009 .

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

[31]  Meredith Ringel Morris,et al.  CoSearch: a system for co-located collaborative web search , 2008, CHI.

[32]  Pernilla Qvarfordt,et al.  Collaborative Information Seeking , 2009, Computer.

[33]  C. Lee Giles,et al.  Context and Page Analysis for Improved Web Search , 1998, IEEE Internet Comput..

[34]  Barry Smyth,et al.  Towards a reputation-based model of social web search , 2010, IUI '10.

[35]  Meredith Ringel Morris,et al.  What do people ask their social networks, and why?: a survey study of status message q&a behavior , 2010, CHI.

[36]  Wolfgang Nejdl,et al.  PROS: A Personalized Ranking Platform for Web Search , 2004, AH.

[37]  Barry Smyth,et al.  Google Shared. A Case-Study in Social Search , 2009, UMAP.

[38]  Peter Pirolli,et al.  Exploring the cognitive consequences of social search , 2009, CHI Extended Abstracts.

[39]  Andrew McCallum,et al.  Learning to Create Customized Authority Lists , 2000, ICML.

[40]  Alan F. Smeaton,et al.  Collaborative video searching on a tabletop , 2007, Multimedia Systems.