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Temporal latent topic user profiles for search personalisation

Vu, Thanh; Willis, Alistair; Tran, Son N. and Song, Dawei (2015). Temporal latent topic user profiles for search personalisation. In: Advances in Information Retrieval (Hanbury, Allan; Kazai, Gabriella; Rauber, Andreas and Fuhr, Norbert eds.), Lecture Notes in Computer Science, Springer International Publishing, pp. 605–616.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-16354-3_67
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Abstract

The performance of search personalisation largely depends on how to build user profiles effectively. Many approaches have been developed to build user profiles using topics discussed in relevant documents, where the topics are usually obtained from human-generated online ontology such as Open Directory Project. The limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the human-generated topics require expensive manual effort to determine the correct categories for each document. This paper addresses these problems by using Latent Dirichlet Allocation for unsupervised extraction of the topics from documents. With the learned topics, we observe that the search intent and user interests are dynamic, i.e., they change from time to time. In order to evaluate the effectiveness of temporal aspects in personalisation, we apply three typical time scales for building a long-term profile, a daily profile and a session profile. In the experiments, we utilise the profiles to re-rank search results returned by a commercial web search engine. Our experimental results demonstrate that our temporal profiles can significantly improve the ranking quality. The results further show a promising effect of temporal features in correlation with click entropy and query position in a search session.

Item Type: Conference or Workshop Item
Copyright Holders: 2015 Springer International Publishing Switzerland
ISBN: 3-319-16353-1, 978-3-319-16353-6
ISSN: 0302-9743
Keywords: user profiles; temporal aspects; latent topics; search personalisation; re-ranking
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: 42303
Depositing User: Thanh Vu
Date Deposited: 09 Mar 2015 09:52
Last Modified: 02 May 2018 14:06
URI: http://oro.open.ac.uk/id/eprint/42303
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