2013
Journal article  Open Access

Discovering Tasks from Search Engine Query Logs

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

user task discovery  query clustering  Management and Accounting  Information Systems  collective task discovery  user tasks  Design  user search intent  Algorithms  collective tasks  Computer Science Applications  Query log analysis  General Business  user search session boundaries  Experimentation 

Although Web search engines still answer user queries with lists of ten blue links to webpages, people are increasingly issuing queries to accomplish their daily tasks (e. g., finding a recipe, booking a flight, reading online news, etc.). In this work, we propose a two-step methodology for discovering tasks that users try to perform through search engines. First, we identify user tasks from individual user sessions stored in search engine query logs. In our vision, a user task is a set of possibly noncontiguous queries (within a user search session), which refer to the same need. Second, we discover collective tasks by aggregating similar user tasks, possibly performed by distinct users. To discover user tasks, we propose query similarity functions based on unsupervised and supervised learning approaches. We present a set of query clustering methods that exploit these functions in order to detect user tasks. All the proposed solutions were evaluated on a manually-built ground truth, and two of them performed better than state-of-the-art approaches. To detect collective tasks, we propose four methods that cluster previously discovered user tasks, which in turn are represented by the bag-of-words extracted from their composing queries. These solutions were also evaluated on another manually-built ground truth.

Source: ACM transactions on information systems 31 (2013): 1–43. doi:10.1145/2493175.2493179

Publisher: Association for Computing Machinery,, New York, NY , Stati Uniti d'America


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BibTeX entry
@article{oai:it.cnr:prodotti:295727,
	title = {Discovering Tasks from Search Engine Query Logs},
	author = {Lucchese C. and Orlando S. and Perego R. and Silvestri F. and Tolomei G},
	publisher = {Association for Computing Machinery,, New York, NY , Stati Uniti d'America},
	doi = {10.1145/2493175.2493179},
	journal = {ACM transactions on information systems},
	volume = {31},
	pages = {1–43},
	year = {2013}
}

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