Conference article  Open Access

Identifying task-based sessions in search engine query logs

Lucchese C., Orlando S., Perego R., Silvestri F., Tolomei G.

Task-based session  Query log analysis  Query clustering  Query log session detection  User search intent 

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.

Source: Fourth ACM International Conference on Web Search and Data Mining, pp. 277–286, Hong Kong, China, 10-12 Febbraio 2011

Publisher: ACM Press, New York, USA


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BibTeX entry
	title = {Identifying task-based sessions in search engine query logs},
	author = {Lucchese C. and Orlando S. and Perego R. and Silvestri F. and Tolomei G.},
	publisher = {ACM Press, New York, USA},
	doi = {10.1145/1935826.1935875},
	booktitle = {Fourth ACM International Conference on Web Search and Data Mining, pp. 277–286, Hong Kong, China, 10-12 Febbraio 2011},
	year = {2011}

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