On Cross-Domain Transfer in Venue Recommendation

Manotumruksa, J., Rafailidis, D., Macdonald, C. and Ounis, I. (2019) On Cross-Domain Transfer in Venue Recommendation. In: 41st European Conference on Information Retrieval (ECIR 2019), Cologne, Germany, 14-18 Apr 2019, pp. 443-456. ISBN 9783030157111 (doi: 10.1007/978-3-030-15712-8_29)

Abstract

Venue recommendation strategies are built upon Collaborative Filtering techniques that rely on Matrix Factorisation (MF), to model users’ preferences. Various cross-domain strategies have been proposed to enhance the effectiveness of MF-based models on a target domain, by transferring knowledge from a source domain. Such cross-domain recommendation strategies often require user overlap, that is common users on the different domains. However, in practice, common users across different domains may not be available. To tackle this problem, recently, several cross-domains strategies without users’ overlaps have been introduced. In this paper, we investigate the performance of state-of-the-art cross-domain recommendation that do not require overlap of users for the venue recommendation task on three large Location-based Social Networks (LBSN) datasets. Moreover, in the context of cross-domain recommendation we extend a state-of-the-art sequential-based deep learning model to boost the recommendation accuracy. Our experimental results demonstrate that state-of-the-art cross-domain recommendation does not clearly contribute to the improvements of venue recommendation systems, and, further we validate this result on the latest sequential deep learning-based venue recommendation approach. Finally, for reproduction purposes we make our implementations publicly available.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Macdonald, Professor Craig and Manotumruksa, Mr Jarana and Ounis, Professor Iadh
• Authors: Manotumruksa, J., Rafailidis, D., Macdonald, C., and Ounis, I.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 0302-9743
• ISBN: 9783030157111
• Published Online: 07 April 2019
• Copyright Holders: Copyright © 2019 Springer Nature Switzerland AG
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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