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Modelling time-aware search tasks for search personalisation

Vu, Thanh; Willis, Alistair and Song, Dawei (2015). Modelling time-aware search tasks for search personalisation. In: WWW '15 Companion, ACM, pp. 131–132.

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URL: http://www.www2015.it/documents/proceedings/compan...
DOI (Digital Object Identifier) Link: https://doi.org/10.1145/2740908.2742714
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Abstract

Recent research has shown that mining and modelling search tasks helps improve the performance of search personalisation. Some approaches have been proposed to model a search task using topics discussed in relevant documents, where the topics are usually obtained from human-generated online ontology such as Open Directory Project. A limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the previous studies largely ignored the dynamic nature of the search task; with the change of time, the search intent and user interests may also change. This paper addresses these problems by modelling search tasks with time-awareness using latent topics, which are automatically extracted from the task's relevance documents by an unsupervised topic modelling method (i.e., Latent Dirichlet Allocation). In the experiments, we utilise the time-aware search task to re-rank result list returned by a commercial search engine and demonstrate a significant improvement in the ranking quality.

Item Type: Conference or Workshop Item
Copyright Holders: 2015 The Authors
ISBN: 1-4503-3473-3, 978-1-4503-3473-0
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 42214
Depositing User: Dawei Song
Date Deposited: 24 Aug 2015 08:39
Last Modified: 30 Nov 2016 11:09
URI: http://oro.open.ac.uk/id/eprint/42214
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