Building user profiles from topic models for personalised search

Morgan Harvey, Fabio Crestani, Mark J. Carman

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

60 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 22nd ACM International Conference on Information & Knowledge Management
Place of PublicationNew York
PublisherACM
Pages2309-2314
ISBN (Print)978-1-4503-2263-8
DOIs
Publication statusPublished - 2013
Event22nd {ACM} International Conference on Information and Knowledge Management, CIKM'13, San Francisco, CA, USA, October 27 - November 1, 2013 -
Duration: 1 Jan 2013 → …

Conference

Conference22nd {ACM} International Conference on Information and Knowledge Management, CIKM'13, San Francisco, CA, USA, October 27 - November 1, 2013
Period1/01/13 → …

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