Building user profiles from topic models for personalised search

Harvey, Morgan, Crestani, Fabio and Carman, Mark J. (2013) Building user profiles from topic models for personalised search. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. Association for Computing Machinery, New York, pp. 2309-2314. ISBN 978-1-4503-2263-8

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Personalisation is an important area in the field of IR that attempts to adapt ranking algorithms so that the results returned are tuned towards the searcher's interests. In this work we use query logs to build personalised ranking models in which user profiles are constructed based on the representation of clicked documents over a topic space. Instead of employing a human-generated ontology, we use novel latent topic models to determine these topics. Our experiments show that by subtly introducing user profiles as part of the ranking algorithm, rather than by re-ranking an existing list, we can provide personalised ranked lists of documents which improve significantly over a non-personalised baseline. Further examination shows that the performance of the personalised system is particularly good in cases where prior knowledge of the search query is limited.

Item Type: Book Section
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Morgan Harvey
Date Deposited: 27 Apr 2015 14:22
Last Modified: 10 Oct 2019 23:15

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