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.
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- 1.
Optionally, it is also possible to consider the latest occurence thereof.
- 2.
While this is an “optimistic” assumption, other variants can be also considered, in particular min operator (“pessimistic” variant) or some other aggregation operators.
- 3.
However other options are possible, what is envisaged in the continuation work.
- 4.
The choice of the value of k is an independent interesting problem that depends on the level of ambiguity of the query and some other external conditions such as the space available for presenting the suggestions in the front-end application. We do not study this problem here and treat k as an external parameter.
- 5.
Such a direct approach can be of high computational complexity though.
- 6.
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Acknowledgments
The work is partially supported by Polish National Science Centre 2012/07/B/ST6/01239 “DISQUSS” grant.
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Sydow, M., Muntean, C.I., Nardini, F.M., Matwin, S., Silvestri, F. (2015). MUSETS: Diversity-Aware Web Query Suggestions for Shortening User Sessions. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_26
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DOI: https://doi.org/10.1007/978-3-319-25252-0_26
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