We propose MUSETS (multi-session total shortening) - a novel formulation of the query suggestion task, specified as an optimization problem. Given an ambiguous user query, the goal is to propose the user a set of query suggestions that optimizes a diversity-aware objective function. The function models the expected number of query reformulations that a user would save until reaching a satisfactory query formulation. The function is diversity-aware, as it naturally enforces high coverage of different alternative continuations of the user session. For modeling the topics covered by the queries, we also use an extended query representation based on entities extracted from Wikipedia. We apply a machine learning approach to learn the model on a set of user sessions to be subsequently used for queries that are under-represented in historical query logs and present an evaluation of the approach.

MUSETS: Diversity-aware web query suggestions for shortening user sessions

Nardini FM;
2015

Abstract

We propose MUSETS (multi-session total shortening) - a novel formulation of the query suggestion task, specified as an optimization problem. Given an ambiguous user query, the goal is to propose the user a set of query suggestions that optimizes a diversity-aware objective function. The function models the expected number of query reformulations that a user would save until reaching a satisfactory query formulation. The function is diversity-aware, as it naturally enforces high coverage of different alternative continuations of the user session. For modeling the topics covered by the queries, we also use an extended query representation based on entities extracted from Wikipedia. We apply a machine learning approach to learn the model on a set of user sessions to be subsequently used for queries that are under-represented in historical query logs and present an evaluation of the approach.
2015
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
978-3-319-25251-3
Diversity
Learning to rank
Query logs
Session shortening
Web query suggestions
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/341410
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