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2016
ACM

Beyond Relevance: Adapting Exploration/Exploitation in Information Retrieval

4 years 3 months ago
Beyond Relevance: Adapting Exploration/Exploitation in Information Retrieval
We present a novel adaptation technique for search engines to better support information-seeking activities that include both lookup and exploratory tasks. Building on previous findings, we describe (1) a classifier that recognizes task type (lookup vs. exploratory) as a user is searching and (2) a reinforcement learning based search engine that adapts accordingly the balance of exploration/exploitation in ranking the documents. This allows supporting both task types surreptitiously without changing the familiar list-based interface. Search results include more diverse results when users are exploring and more precise results for lookup tasks. Users found more useful results in exploratory tasks when compared to a baseline system, which is specifically tuned for lookup tasks. Author Keywords Exploratory search; models of search behavior; reinforcement learning; lookup search; adaptive systems. ACM Classification Keywords H.5.m. Information Interfaces and Presentation (e.g. HCI): M...
Kumaripaba Athukorala, Alan Medlar, Antti Oulasvir
Added 06 Apr 2016
Updated 06 Apr 2016
Type Journal
Year 2016
Where IUI
Authors Kumaripaba Athukorala, Alan Medlar, Antti Oulasvirta, Giulio Jacucci, Dorota Glowacka
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