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

Authors:
Deirdre Lungley;Udo Kruschwitz;Dawei Song
Affiliations:
University of Essex, Colchester, UK;University of Essex, Colchester, UK;Robert Gordon University, Aberdeen, UK
Venue:
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Year:
2012

Citing 4
Cited 0

Deriving concept hierarchies from text

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Formal Concept Analysis: Mathematical Foundations

Formal Concept Analysis: Mathematical Foundations
Query chains: learning to rank from implicit feedback

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering search engine related queries using association rules

Journal of Web Engineering

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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.