PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Sydow M., Muntean C. I., Nardini F. M., Matwin S., Silvestri F. MUSETS: diversity-aware web query suggestions for shortening user sessions. In: ISMIS 2015 - Foundations of Intelligent Systems. 22nd International Symposium (Lyon, France, 21-23 October 2015). Proceedings, pp. 237 - 247. Floriana Esposito et al... (eds.). (Lecture Notes in Computer Science, vol. 9384). Springer, 2015.
 
 
Abstract
(English)
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.
URL: http://link.springer.com/chapter/10.1007/978-3-319-25252-0_26
DOI: 10.1007/978-3-319-25252-0_26
Subject Web query suggestions
Diversity
Session shortening
Query logs
Learning to rank
H.3.3 Information Search and Retrieval
68U35 Information systems


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