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A unified posterior regularized topic model with maximum margin for learning-to-rank

Jameel, Shoaib, Lam, Wai, Schockaert, Steven ORCID: and Bing, Lidong 2015. A unified posterior regularized topic model with maximum margin for learning-to-rank. Presented at: CIKM 2015: Conference on Information and Knowledge Management, Melbourne, Australia, 19-23 October 2015. CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, pp. 103-112. 10.1145/2806416.2806482

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While most methods for learning-to-rank documents only consider relevance scores as features, better results can often be obtained by taking into account the latent topic structure of the document collection. Existing approaches that consider latent topics follow a two-stage approach, in which topics are discovered in an unsupervised way, as usual, and then used as features for the learning-to-rank task. In contrast, we propose a learning-to-rank framework which integrates the supervised learning of a maximum margin classifier with the discovery of a suitable probabilistic topic model. In this way, the labelled data that is available for the learning-to-rank task can be exploited to identify the most appropriate topics. To this end, we use a unified constrained optimization framework, which can dynamically compute the latent topic similarity score between the query and the document. Our experimental results show a consistent improvement over the state-of-the-art learning-to-rank models.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: ACM
ISBN: 9781450337946
Funders: ERC
Date of First Compliant Deposit: 13 December 2017
Last Modified: 31 Oct 2022 10:25

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