Contributo in atti di convegno, 2011, ENG, 10.1145/1935826.1935875
Lucchese C.; Orlando S.; Perego R.; Silvestri F.; Tolomei G.
CNR-ISTI, Pisa, Italy; Department of Computer Science, University of Venice, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy
The research challenge addressed in this paper is to devise effective techniques for identifying task-based sessions, i.e. sets of possibly non contiguous queries issued by the user of a Web Search Engine for carrying out a given task. In order to evaluate and compare different approaches, we built, by means of a manual labeling process, a ground-truth where the queries of a given query log have been grouped in tasks. Our analysis of this ground-truth shows that users tend to perform more than one task at the same time, since about 75% of the submitted queries involve a multi-tasking activity. We formally define the Task-based Session Discovery Problem (TSDP) as the problem of best approximating the manually annotated tasks, and we propose several variants of well known clustering algorithms, as well as a novel efficient heuristic algorithm, specifically tuned for solving the TSDP. These algorithms also exploit the collaborative knowledge collected by Wiktionary and Wikipedia for detecting query pairs that are not similar from a lexical content point of view, but actually semantically related. The proposed algorithms have been evaluated on the above ground-truth, and are shown to perform better than state-of-the-art approaches, because they effectively take into account the multi-tasking behavior of users.
Fourth ACM International Conference on Web Search and Data Mining, pp. 277–286, Hong Kong, China, 10-12 Febbraio 2011
Query log analysis, Query log session detection, Task-based session, Query clustering, User search intent
Tolomei Gabriele, Silvestri Fabrizio, Lucchese Claudio, Perego Raffaele
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 199327
Year: 2011
Type: Contributo in atti di convegno
Creation: 2013-01-18 17:56:09.000
Last update: 2017-10-16 10:51:23.000
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
URL: http://portal.acm.org/citation.cfm?doid=1935826.1935875
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:199327
DOI: 10.1145/1935826.1935875
Scopus: 2-s2.0-79952409419