Learning Adaptive Domain Models from Click Data to Bootstrap Interactive Web Search

摘要

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.