Contributo in atti di convegno, 2015, ENG, 10.1007/978-3-319-25252-0_26
Sydow M.; Muntean C.I.; Nardini F.M.; Matwin S.; Silvestri F.
Institute of Computer Science,Polish Academy of Sciences and Polish-Japanese Institute of Information Technology, Warsaw, Poland; CNR-ISTI, Pisa, Italy; Big Data Institute, Dalhousie University, Halifax, Canada, USA;
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.
Foundations of Intelligent Systems. 22nd International Symposium, pp. 237–247, Lyon, France, 21-23/10/2015
Diversity, Learning to rank, Query logs, Session shortening, Web query suggestions
Nardini Franco Maria, Muntean Cristina
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 345225
Year: 2015
Type: Contributo in atti di convegno
Creation: 2016-01-22 17:02:54.000
Last update: 2020-10-21 15:22:13.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1007/978-3-319-25252-0_26
URL: https://link.springer.com/chapter/10.1007/978-3-319-25252-0_26
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:345225
DOI: 10.1007/978-3-319-25252-0_26
Scopus: 2-s2.0-84951942644