Learning adaptive domain models from click data to bootstrap interactive web search

Deirdre Lungley; Udo Kruschwitz; Dawei Song

doi:10.1007/978-3-642-28997-2_56

Learning adaptive domain models from click data to bootstrap interactive web search

Deirdre Lungley^*, Udo Kruschwitz, Dawei Song

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Today, searchers exploring the World Wide Web have come to expect enhanced search interfaces-query completion and related searches have become standard. Here we propose a Formal Concept Analysis lattice as an underlying domain model to provide a source of query refinements. The initial lattice is constructed using NLP. User clicks on documents, seen as implicit user feedback, are harnessed to adapt it. In this paper, we explore the viability of this adaptation process and the results we present demonstrate its promise and limitations for proposing initial effective refinements when searching the diverse WWW domain.

Original language	English
Title of host publication	Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Proceedings
Pages	527-530
Number of pages	4
DOIs	https://doi.org/10.1007/978-3-642-28997-2_56
Publication status	Published - 2012
Externally published	Yes
Event	34th European Conference on Information Retrieval, ECIR 2012 - Barcelona, Spain Duration: 1 Apr 2012 → 5 Apr 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7224 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	34th European Conference on Information Retrieval, ECIR 2012
Country/Territory	Spain
City	Barcelona
Period	1/04/12 → 5/04/12

Keywords

Domain Modelling
Formal Concept Analysis
Query Refinement
Usage Mining

Access to Document

10.1007/978-3-642-28997-2_56

Cite this

Lungley, D., Kruschwitz, U., & Song, D. (2012). Learning adaptive domain models from click data to bootstrap interactive web search. In Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Proceedings (pp. 527-530). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7224 LNCS). https://doi.org/10.1007/978-3-642-28997-2_56

Lungley, Deirdre ; Kruschwitz, Udo ; Song, Dawei. / Learning adaptive domain models from click data to bootstrap interactive web search. Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Proceedings. 2012. pp. 527-530 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2c39678315d7442ea44ed2fad58e6a6a,

title = "Learning adaptive domain models from click data to bootstrap interactive web search",

abstract = "Today, searchers exploring the World Wide Web have come to expect enhanced search interfaces-query completion and related searches have become standard. Here we propose a Formal Concept Analysis lattice as an underlying domain model to provide a source of query refinements. The initial lattice is constructed using NLP. User clicks on documents, seen as implicit user feedback, are harnessed to adapt it. In this paper, we explore the viability of this adaptation process and the results we present demonstrate its promise and limitations for proposing initial effective refinements when searching the diverse WWW domain.",

keywords = "Domain Modelling, Formal Concept Analysis, Query Refinement, Usage Mining",

author = "Deirdre Lungley and Udo Kruschwitz and Dawei Song",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "34th European Conference on Information Retrieval, ECIR 2012 ; Conference date: 01-04-2012 Through 05-04-2012",

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Lungley, D, Kruschwitz, U & Song, D 2012, Learning adaptive domain models from click data to bootstrap interactive web search. in Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7224 LNCS, pp. 527-530, 34th European Conference on Information Retrieval, ECIR 2012, Barcelona, Spain, 1/04/12. https://doi.org/10.1007/978-3-642-28997-2_56

Learning adaptive domain models from click data to bootstrap interactive web search. / Lungley, Deirdre; Kruschwitz, Udo; Song, Dawei.
Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Proceedings. 2012. p. 527-530 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7224 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Today, searchers exploring the World Wide Web have come to expect enhanced search interfaces-query completion and related searches have become standard. Here we propose a Formal Concept Analysis lattice as an underlying domain model to provide a source of query refinements. The initial lattice is constructed using NLP. User clicks on documents, seen as implicit user feedback, are harnessed to adapt it. In this paper, we explore the viability of this adaptation process and the results we present demonstrate its promise and limitations for proposing initial effective refinements when searching the diverse WWW domain.

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Lungley D, Kruschwitz U, Song D. Learning adaptive domain models from click data to bootstrap interactive web search. In Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Proceedings. 2012. p. 527-530. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-28997-2_56

Learning adaptive domain models from click data to bootstrap interactive web search

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