@article{zhang2006,
        title = {{Mining search engine query logs for query recommendation}},
        author = {Z. Zhang and O. Nasraoui},
        journal = {Proceedings of the 15th international conference on World Wide Web},
        pages = {1039--1040},
        publisher = {ACM Press New York, NY, USA},
        year = 2006,
        biburl = {http://www.bibsonomy.org/bibtex/2f4873abd71cd109213b349c554cb376d/wnpxrz},
	keywords = {log query recommendersystems recommendation ir search}
}

@inproceedings{243216,
        title = {Reexamining the cluster hypothesis: scatter/gather on retrieval results},
        address = {New York, NY, USA},
        author = {Marti A. Hearst and Jan O. Pedersen},
        booktitle = {SIGIR '96: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval},
        pages = {76--84},
        publisher = {ACM},
        year = 1996,
        url = {http://portal.acm.org/citation.cfm?id=243216},
        location = {Zurich, Switzerland},
	isbn = {0-89791-792-8},
	doi = {http://doi.acm.org/10.1145/243199.243216},
	description = {Reexamining the cluster hypothesis},
	biburl = {http://www.bibsonomy.org/bibtex/29b5a5794e780defff1de777633e97a1b/wnpxrz},
	keywords = {scatter ir search gather imported clustering}
}

@inproceedings{strohmaier2007a,
        title = {How Do Users Express Goals on the Web? - An Exploration of Intentional Structures in Web Search},
        author = {M. Strohmaier and M. Lux and M. Granitzer and P. Scheir and S. Liaskos and E. Yu},
        booktitle = {We Know'07 International Workshop on Collaborative Knowledge Management for Web Information Systems, in conjunction with WISE'07, Nancy, France},
        year = 2007,
        biburl = {http://www.bibsonomy.org/bibtex/28cafb30efce0f039e1978a7a277f59fa/wnpxrz},
	keywords = {search ir user goal web intentional}
}

@inproceedings{Pageetal98,
        title = {The PageRank citation ranking: Bringing order to the Web},
        address = {Brisbane, Australia},
        author = {L. Page and S. Brin and R. Motwani and T. Winograd},
        booktitle = {Proceedings of the 7th International World Wide Web Conference},
        pages = {161--172},
        year = 1998,
        url = {citeseer.nj.nec.com/page98pagerank.html},
        biburl = {http://www.bibsonomy.org/bibtex/2ac49c33e114ca171db40cece6a0ae4d6/wnpxrz},
	keywords = {search pagerank ranking ir}
}

@inproceedings{conf/www/RichardsonPB06,
        title = {Beyond PageRank: machine learning for static ranking.},
        author = {Matthew Richardson and Amit Prakash and Eric Brill},
        booktitle = {WWW},
        crossref = {conf/www/2006},
        editor = {Les Carr and David De Roure and Arun Iyengar and Carole A. Goble and Michael Dahlin},
        pages = {707-715},
        publisher = {ACM},
        year = 2006,
        url = {http://dblp.uni-trier.de/db/conf/www/www2006.html#RichardsonPB06},
        ee = {http://doi.acm.org/10.1145/1135777.1135881},
	isbn = {1-59593-323-9},
	date = {2006-07-17},
	description = {dblp},
	biburl = {http://www.bibsonomy.org/bibtex/24d2ddff0f0013f7d6cffc782c5eca56c/wnpxrz},
	keywords = {ranking machinelearning ir search}
}

@inproceedings{begelman2006clustering,
        title = {Automated Tag Clustering:
Improving search and exploration in the tag space},
        author = {Grigory Begelman and Philipp Keller and Frank Smadja},
        booktitle = {Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland},
        year = 2006,
        id = {699842},
	priority = {3},
	biburl = {http://www.bibsonomy.org/bibtex/276b741061fab004645c3119db5a17bc3/wnpxrz},
	keywords = {clustering collaborative exploration tag search folksonomy filtering tags}
}

@article{LPage1998,
        title = {{T}he {A}natomy of a {L}arge-{S}cale {H}ypertextual {W}eb {S}earch {E}ngine},
        author = {Sergey Brin and Lawrence Page},
        journal = {Computer Networks and ISDN Systems},
        month = {April},
        number = {1-7},
        pages = {107--117},
        volume = 30,
        year = 1998,
        doi = {10.1016/S0169-7552(98)00110-X},
	biburl = {http://www.bibsonomy.org/bibtex/2fc936cec60b1b7ab69f230f14139e8ab/wnpxrz},
	keywords = {search ir web}
}

@inproceedings{Yee2003Faceted,
        title = {Faceted metadata for image search and browsing},
        author = {Ka P. Yee and Kirsten Swearingen and Kevin Li and Marti Hearst},
        booktitle = {Proceedings of the SIGCHI conference on Human factors in computing systems},
        year = 2003,
        id = {1400967},
	priority = {2},
	pdf = {Yee2003Faceted.pdf},
	description = {CiteULike: Faceted metadata for image search and browsing},
	biburl = {http://www.bibsonomy.org/bibtex/2459da9d9a1fff65925c3e96f97fb685a/wnpxrz},
	keywords = {faceted search browsing metadata image}
}

@misc{gauch-ontologybased,
        title = {Ontology-Based User Profiles for Search and Browsing},
        author = {Susan Gauch and Jason Chaffee and Alexander Pretschner},
        year = 2002,
        url = {citeseer.ist.psu.edu/gauch02ontologybased.html},
        description = {Ontology-Based User Profiles for Search and Browsing (ResearchIndex)},
	biburl = {http://www.bibsonomy.org/bibtex/27e876459e3a889402eba7dbbe3595b46/wnpxrz},
	keywords = {profile ir ontology browsing search user imported}
}

@inproceedings{988764,
        title = {Adaptive web search based on user profile constructed without any effort from users},
        address = {New York, NY, USA},
        author = {Kazunari Sugiyama and Kenji Hatano and Masatoshi Yoshikawa},
        booktitle = {WWW '04: Proceedings of the 13th international conference on World Wide Web},
        pages = {675--684},
        publisher = {ACM Press},
        year = 2004,
        url = {http://portal.acm.org/citation.cfm?id=988672.988764},
        location = {New York, NY, USA},
	isbn = {1-58113-844-X},
	doi = {http://doi.acm.org/10.1145/988672.988764},
	description = {Adaptive web search based on user profile constructed without any effort from users},
	abstract = {Web search engines help users find useful information on the World Wide Web (WWW). However, when the same query is submitted by different users, typical search engines return the same result regardless of who submitted the query. Generally, each user has different information needs for his/her query. Therefore, the search result should be adapted to users with different information needs. In this paper, we first propose several approaches to adapting search results according to each user's need for relevant information without any user effort, and then verify the effectiveness of our proposed approaches. Experimental results show that search systems that adapt to each user's preferences can be achieved by constructing user profiles based on modified collaborative filtering with detailed analysis of user's browsing history in one day.},
	biburl = {http://www.bibsonomy.org/bibtex/23db95ec7dc4631629b638eb0230cc55a/wnpxrz},
	keywords = {web search adaptive profile ir profiling imported user}
}

@inproceedings{freyne2007,
        title = {Collecting community wisdom: integrating social search & social navigation},
        address = {New York, NY, USA},
        author = {Jill Freyne and Rosta Farzan and Peter Brusilovsky and Barry Smyth and Maurice Coyle},
        booktitle = {IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces},
        pages = {52--61},
        publisher = {ACM Press},
        year = 2007,
        url = {http://portal.acm.org/citation.cfm?doid=1216295.1216312},
        location = {Honolulu, Hawaii, USA},
	isbn = {1-59593-481-2},
	doi = {http://doi.acm.org/10.1145/1216295.1216312},
	description = {Collecting community wisdom},
	abstract = {The goal of this paper is to detail the integration of two "social Web" technologies - social search and social navigation - and to highlight the benefits of such integration on two levels. Firstly, both technologies harvest and harness "community wisdom" and in an integrated system each of the search and navigation components can benefit from the additional community wisdom gathered by the other when assisting users to locate relevant information. Secondly, by integrating search and browsing we facilitate the development of a unique interface that effectively blends search and browsing functionality as part of a seamless social information access service. This service allows users to effectively combine their search and browsing behaviors. In this paper we will argue that this integration provides significantly more than the simple sum of the parts.},
	biburl = {http://www.bibsonomy.org/bibtex/288603ee0903b30dc642aebdaa6a22f93/wnpxrz},
	keywords = {search navigation social}
}

@inproceedings{1288560,
        title = {Blog search and mining in the business domain},
        address = {New York, NY, USA},
        author = {Yun Chen and Flora S. Tsai and Kap Luk Chan},
        booktitle = {DDDM '07: Proceedings of the 2007 international workshop on Domain driven data mining},
        pages = {55--60},
        publisher = {ACM Press},
        year = 2007,
        url = {http://portal.acm.org/citation.cfm?id=1288552.1288560&coll=GUIDE&dl=},
        location = {San Jose, California},
	isbn = {978-1-59593-846-6},
	doi = {http://doi.acm.org/10.1145/1288552.1288560},
	description = {Blog search and mining in the business domain},
	biburl = {http://www.bibsonomy.org/bibtex/28add5688facb791bda4e6db738eb1654/wnpxrz},
	keywords = {blog ir business mining search}
}

@misc{allan97interactive,
        title = {Interactive Cluster Visualization for Information Retrieval},
        author = {J. Allan and A. Leouski and R. Swan},
        year = 1997,
        url = {citeseer.ist.psu.edu/allan97interactive.html},
        description = {Interactive Cluster Visualization for Information Retrieval - Allan, Leouski, Swan (ResearchIndex)},
	abstract = {This study investigates the ability of cluster visualization to help a user rapidly identify relevant documents. It provides added support for the truth of the Cluster Hypothesis on retrieved documents and shows that clustering of relevant documents is readily visible. The study then shows the visual effect of a technique similar to relevance feedback and shows how to enhance that effect to further help the user locate relevant material. A ranked list returned by a text search engine purports...},
	biburl = {http://www.bibsonomy.org/bibtex/21e812543ac2929cf85e5ee475efe3c36/wnpxrz},
	keywords = {ir clustering search visualization proj:et imported}
}

@article{whittle2006,
        title = {{Query transformations and their role in Web searching by the general public}},
        author = {M. Whittle and B. Eaglestone and N. Ford and V.J. Gillet and A. Madden},
        journal = {Information Research},
        number = 1,
        volume = 12,
        year = 2006,
        biburl = {http://www.bibsonomy.org/bibtex/2a3a38920006837a63ccfdcd7eda4d3fe/wnpxrz},
	keywords = {web search query ir}
}

@inproceedings{salton1988spreading,
        title = {On the use of spreading activation methods in automatic information},
        address = {New York, NY, USA},
        author = {G. Salton and C. Buckley},
        booktitle = {SIGIR '88: Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval},
        pages = {147--160},
        publisher = {ACM Press},
        year = 1988,
        url = {http://portal.acm.org/citation.cfm?id=62447&dl=ACM&coll=GUIDE},
        location = {Grenoble, France},
	isbn = {2-7061-0309-4},
	doi = {http://doi.acm.org/10.1145/62437.62447},
	description = {On the use of spreading activation methods in automatic information},
	abstract = {Spreading activation methods have been recommended in information retrieval to expand the search vocabulary and to complement the retrieved document sets. The spreading activation strategy is reminiscent of earlier associative indexing and retrieval systems. Some spreading activation procedures are briefly described, and evaluation output is given, reflecting the effectiveness of one of the proposed procedures.},
	biburl = {http://www.bibsonomy.org/bibtex/2994aef0486e69095ee0d8ba5b3e3a91c/wnpxrz},
	keywords = {ir spreading search}
}

@inproceedings{chakrabarti2007dynamic,
        title = {Dynamic personalized pagerank in entity-relation graphs},
        address = {New York, NY, USA},
        author = {Soumen Chakrabarti},
        booktitle = {WWW '07: Proceedings of the 16th international conference on World Wide Web},
        pages = {571--580},
        publisher = {ACM Press},
        year = 2007,
        url = {http://portal.acm.org/citation.cfm?id=1242572.1242650&coll=GUIDE&dl=GUIDE&type=series&idx=1242572&part=Proceedings&WantType=Proceedings&title=International%20World%20Wide%20Web%20Conference&CFID=15151515&CFTOKEN=6184618},
        location = {Banff, Alberta, Canada},
	isbn = {978-1-59593-654-7},
	doi = {http://doi.acm.org/10.1145/1242572.1242650},
	description = {: WWW '07, Dynamic personalized pagerank in ...},
	biburl = {http://www.bibsonomy.org/bibtex/26edef588be76947d64884e2bc7f3089e/wnpxrz},
	keywords = {dynamic ir pagerank ranking personalization search}
}

@article{lawrence98searching,
        title = {Searching the world wide web},
        author = {S. Lawrence and C. Giles},
        journal = {Science},
        month = 04,
        pages = {98-100},
        volume = 280,
        year = 1998,
        day = 03,
        url = {http://citeseer.comp.nus.edu.sg/lawrence98searching.html},
        abstract = {The coverage and recency of the major World Wide Web search engines was analyzed, yielding some surprising results. The coverage of any one engine is significantly limited: No single engine indexes more than about one-third of the "indexable Web," the coverage of the six engines investigated varies by an order of magnitude, and
combining the results of the six engines yields about 3.5 times as many documents on average as compared with the results from only one engine. Analysis of the overlap between pairs of engines gives an estimated lower bound on the size of the indexable Web of 320 million pages.},
	biburl = {http://www.bibsonomy.org/bibtex/2c77ff07c7801271cd31e54a850edf430/wnpxrz},
	keywords = {ir web search}
}

@inproceedings{HothoJäschke+:ESWC06,
        title = {Information Retrieval in Folksonomies: Search and Ranking},
        author = {Andreas Hotho and Robert Jäschke and Christoph Schmitz and Gerd Stumme},
        booktitle = {European Semantic Web Conference, Budva, Montenegro, 11-14.06.06},
        year = 2006,
        url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/seach2006hotho_eswc.pdf},
        abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.
},
	biburl = {http://www.bibsonomy.org/bibtex/2e66f878addd0832ef8c04a6a93337101/wnpxrz},
	keywords = {search folksonomy ir ranking}
}

@misc{citeulike:105638,
        title = {Identity and Search in Social Networks},
        author = {D. J. Watts and P. S. Dodds and M. E. J. Newman},
        month = {May},
        year = 2002,
        url = {http://arxiv.org/abs/cond-mat/0205383},
        id = {105638},
	priority = {4},
	eprint = {cond-mat/0205383},
	abstract = {Social networks have the surprising property of being "searchable": Ordinary people are capable of directing messages through their network of acquaintances to reach a specific but distant target person in only a few steps. We present a model that offers an explanation of social network searchability in terms of recognizable personal identities: sets of characteristics measured along a number of social dimensions.},
	biburl = {http://www.bibsonomy.org/bibtex/221bbcbb780f14f106cbaa72fa8e9aa22/wnpxrz},
	keywords = {social search identity network}
}

@misc{citeulike:137147,
        title = {How to search a social network},
        author = {Lada A. Adamic and Eytan Adar},
        month = {November},
        year = 2004,
        url = {http://arxiv.org/abs/cond-mat/0310120},
        id = {137147},
	priority = {0},
	eprint = {cond-mat/0310120},
	abstract = {We address the question of how participants in a small world experiment are
able to find short paths in a social network using only local information about
their immediate contacts. We simulate such experiments on a network of actual
email contacts within an organization as well as on a student social networking
website. On the email network we find that small world search strategies using
a contact's position in physical space or in an organizational hierarchy
relative to the target can effectively be used to locate most individuals.
However, we find that in the online student network, where the data is
incomplete and hierarchical structures are not well defined, local search
strategies are less effective. We compare our findings to recent theoretical
hypotheses about underlying social structure that would enable these simple
search strategies to succeed and discuss the implications to social software
design.},
	biburl = {http://www.bibsonomy.org/bibtex/20f695a72a5ccaa986dbdfac5e8126ae3/wnpxrz},
	keywords = {network search social}
}