Information retrieval using query-document pair information

Details Information retrieval using query-document pair information Information retrieval using query-document pair information

2011-01-25

2011-01-25

Lu, Charles E (Department: 2161)

Data processing: database and file management or data structures

Database and file access

Post processing of search results

C707S723000, C707S724000, C707S798000

Reexamination Certificate

active

07877385

ABSTRACT:
Information retrieval using query-document pair information is described. In an embodiment, a click record is accessed having information about queries and documents where user clicks have been observed for query-document pairs. A click graph is either formed or accessed. This has nodes connected by edges, each node representing any of a document and a query and each edge representing at least one observed click. Given at least one first node in the click graph, a similarity measure is determined between that first node and each of one or more second nodes. The second nodes are then ranked on the basis of the similarity measure results and the ranking is used to retrieve information from the click record.

REFERENCES:
patent: 5692107 (1997-11-01), Simoudis et al.
patent: 6151595 (2000-11-01), Pirolli et al.
patent: 6230153 (2001-05-01), Howard et al.
patent: 6549896 (2003-04-01), Candan et al.
patent: 6792419 (2004-09-01), Raghavan
patent: 6871202 (2005-03-01), Broder
patent: 7117208 (2006-10-01), Tamayo et al.
patent: 7143091 (2006-11-01), Charnock et al.
patent: 7181438 (2007-02-01), Szabo
patent: 7251654 (2007-07-01), Eiron et al.
patent: 2001/0047355 (2001-11-01), Anwar
patent: 2003/0220984 (2003-11-01), Jones et al.
patent: 2006/0036598 (2006-02-01), Wu
patent: 2006/0064411 (2006-03-01), Gross et al.
patent: 2006/0095430 (2006-05-01), Zeng et al.
patent: 2007/0143322 (2007-06-01), Kothari et al.
Awan et al. Distributed Uniform Sampling in Unstructured Peer to Peer Networks. © 2006 IEEE.
Agichtein, et al., “Improving Web Search Ranking By Incorporating User Behaviour Information”, ACM, 2006, pp. 8.
Agichtein, et al., “Learning User Interaction Models For Predicting Web Search Result Preferences”, ACM, 2006, pp. 8.
Beeferman, et al., “Agglomerative clustering of a search engine query log”, pp. 10.
Craswell, et al., “Random Walks on the Click Graph”, ACM, 2007, pp. 8.
Fox, et al., “Evaluating Implicit Measures To Improve Web Search”, ACM, vol. 23, No. 2, 2005, pp. 147-168.
Gulli, “On Two Web IR Boosting Tools: Clustering and Ranking”, at <<http://www.di.unipi.it/phd/tesi/tesi—2006/PhDthesis—Gulli.pdf>>, May 6, 2006, pp. 141.
Joachims, et al., “Accurately Interpreting Clickthrough Data As Implicit Feedback”, ACM, 2005, pp. 8.
Joachims, “Optimizing Search Engines Using Clickthrough Data”, ACM, 2002, pp. 10.
Kirsch, “Social Information Retrieval”, at <<http://sites.inka.de/moebius/docs/socialir-thesis.pdf>>, Nov. 2nd, 2005, pp. 104.
Lafferty, et al., “Document Language Models, Query Models, And Risk Minimization For Information Retrieval”, ACM, 2001, pp. 9.
Shi, et al., “Normalized Cuts And Image Segmentation”, IEEE, 1997, pp. 731-737.
Szummer, et al., “Partially Labelled Classification With Markov Random Walks”, pp. 8.
Thompson, et al., “Query Expansion Using Random Walk Models”, ACM, 2005, pp, 8.
Tishby, et al., “Data clustering by Markovian relaxation and the information bottleneck method”, pp. 7.
Toutanova, et al., “Learning Random Walk Models For Inducing Word Dependency Distributions”, 2004, pp. 8.
Upstill, “Document ranking using web evidence”, at <<http://www.webir.org/resources/phd/Upstill—2005.pdf>>, Trystan Garrett Upstill, Aug. 2005, pp. 244.
Wen, et al., “Clustering User Queries Of A Search Engine”, ACM, 2001, pp. 162-168.
Wenyin, et al., “Semi-Automatic Image Annotation”, pp. 8.
Xue, et al., “Optimizing Web Search Using Web Click-Through Data”, ACM, 2004, pp. 118-126.
Yates, et al., “Modeling user search behaviour”, IEEE, 2005, pp. 10.

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