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Cross-domain ranking via latent space learning

Cross-domain ranking via latent space learning
Cross-domain ranking via latent space learning
We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we present a unified model: BayCDR for cross-domain ranking. BayCDR uses a latent space to measure the correlation between different domains, and learns the ranking functions from the interrelated domains via the latent space by a Bayesian model, where each ranking function is based on a weighted average model. An efficient learning algorithm based on variational inference and a generalization bound has been developed. To scale up to handle real large data, we also present a learning algorithm under the Map-Reduce programming model. Finally, we demonstrate the effectiveness and efficiency of BayCDR on large datasets.
cross-domain ranking, heterogeneous ranking, Machine Learning
2618-2624
AAAI Press
Tang, Jie
69c44bae-b1fa-45eb-a01d-3ac5b00fa749
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c
Tang, Jie
69c44bae-b1fa-45eb-a01d-3ac5b00fa749
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c

Tang, Jie and Hall, Wendy (2017) Cross-domain ranking via latent space learning. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence and the Twenty-Ninth Innovative Applications of Artificial Intelligence Conference. AAAI Press. pp. 2618-2624 .

Record type: Conference or Workshop Item (Paper)

Abstract

We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we present a unified model: BayCDR for cross-domain ranking. BayCDR uses a latent space to measure the correlation between different domains, and learns the ranking functions from the interrelated domains via the latent space by a Bayesian model, where each ranking function is based on a weighted average model. An efficient learning algorithm based on variational inference and a generalization bound has been developed. To scale up to handle real large data, we also present a learning algorithm under the Map-Reduce programming model. Finally, we demonstrate the effectiveness and efficiency of BayCDR on large datasets.

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Accepted/In Press date: 11 November 2016
Published date: 13 February 2017
Venue - Dates: Thirty-First AAAI Conference on Artificial Intelligence, , San Francisco, United States, 2017-02-04 - 2017-02-09
Keywords: cross-domain ranking, heterogeneous ranking, Machine Learning
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 411954
URI: http://eprints.soton.ac.uk/id/eprint/411954
PURE UUID: 4d359b34-be73-441f-95a5-d44d485ab3d6
ORCID for Wendy Hall: ORCID iD orcid.org/0000-0003-4327-7811

Catalogue record

Date deposited: 03 Jul 2017 16:31
Last modified: 15 Sep 2021 01:34

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Contributors

Author: Jie Tang
Author: Wendy Hall ORCID iD

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