ECAI 2004 Conference Paper

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Improving Web Search through Collaborative Query Recommendation

Evelyn Balfe, Barry Smyth

Search engines are the primary means by which people locate information on the Web. Unfortunately most Web users are not information retrieval experts and there is a tendency for Web queries to be ambiguous and under-specified. Query expansion and recommendation techniques offer one way to solve the ambiguous query problem in Web search, by automatically identifying and adding new terms to a vague query in order to focus the search. In this paper, we describe and evaluate a novel query recommendation technique based on reusing previous search histories. The central idea is the recommendation of queries to a user. This is achieved by selecting, ranking, and then recommending previously successful queries to users. Its novelty stems from the way in which queries are scored and ranked using relevance and coverage factors in order to prioritise those queries that are most likely to be successful in the current search context. We demonstrate that these recommendations can lead to improved search performance based on live-user data.

Keywords: Web Search, Relevance, Ranking, Evaluation

Citation: Evelyn Balfe, Barry Smyth: Improving Web Search through Collaborative Query Recommendation. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.268-272.

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ECAI-2004 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Universitat Politècnica de València on behalf of Asociación Española de Inteligencia Artificial (AEPIA) and Associació Catalana d'Intel-ligència Artificial (ACIA).