A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation

Manotumruksa, J., Macdonald, C. and Ounis, I. (2017) A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation. In: 26th ACM International Conference on Information and Knowledge Management (CIKM 2017), Singapore, 6-10 Nov 2017, pp. 1469-1478. ISBN 9781450349185 (doi:10.1145/3132847.3132985)

Manotumruksa, J., Macdonald, C. and Ounis, I. (2017) A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation. In: 26th ACM International Conference on Information and Knowledge Management (CIKM 2017), Singapore, 6-10 Nov 2017, pp. 1469-1478. ISBN 9781450349185 (doi:10.1145/3132847.3132985)

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Abstract

Recommending a ranked list of interesting venues to users based on their preferences has become a key functionality in Location-Based Social Networks (LBSNs) such as Yelp and Gowalla. Bayesian Personalised Ranking (BPR) is a popular pairwise recommendation technique that is used to generate the ranked list of venues of interest to a user, by leveraging the user's implicit feedback such as their check-ins as instances of positive feedback, while randomly sampling other venues as negative instances. To alleviate the sparsity that affects the usefulness of recommendations by BPR for users with few check-ins, various approaches have been proposed in the literature to incorporate additional sources of information such as the social links between users, the textual content of comments, as well as the geographical location of the venues. However, such approaches can only readily leverage one source of additional information for negative sampling. Instead, we propose a novel Personalised Ranking Framework with Multiple sampling Criteria (PRFMC) that leverages both geographical influence and social correlation to enhance the effectiveness of BPR. In particular, we apply a multi-centre Gaussian model and a power-law distribution method, to capture geographical influence and social correlation when sampling negative venues, respectively. Finally, we conduct comprehensive experiments using three large-scale datasets from the Yelp, Gowalla and Brightkite LBSNs. The experimental results demonstrate the effectiveness of fusing both geographical influence and social correlation in our proposed PRFMC framework and its superiority in comparison to BPR-based and other similar ranking approaches. Indeed, our PRFMC approach attains a 37% improvement in MRR over a recently proposed approach that identifies negative venues only from social links.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Manotumruksa, Mr Jarana and Ounis, Professor Iadh
Authors: Manotumruksa, J., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781450349185
Copyright Holders:Copyright © 2017 ACM
First Published:First published in Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM 2017): 1469-1478
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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