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 language | English |
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Title of host publication | Proceedings of the 22nd ACM International Conference on Information & Knowledge Management |
Place of Publication | New York |
Publisher | ACM |
Pages | 2309-2314 |
ISBN (Print) | 978-1-4503-2263-8 |
DOIs | |
Publication status | Published - 2013 |
Event | 22nd {ACM} International Conference on Information and Knowledge Management, CIKM'13, San Francisco, CA, USA, October 27 - November 1, 2013 - Duration: 1 Jan 2013 → … |
Conference
Conference | 22nd {ACM} International Conference on Information and Knowledge Management, CIKM'13, San Francisco, CA, USA, October 27 - November 1, 2013 |
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Period | 1/01/13 → … |