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Who wants to join me? Companion recommendation in location based social networks

Liao, Yi, Lam, Wai, Jameel, Shoaib, Schockaert, Steven and Xie, Xing 2016. Who wants to join me? Companion recommendation in location based social networks. Presented at: ACM International Conference on the Theory of Information Retrieval, Newark, DE, 12 - 16 September 2016. Published in: Carterette, Ben, Fang, Hui, Lalmas, Mounia and Jian-Yun, Nie eds. ICTIR '16 Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval. New York, NY: ACM, pp. 271-280. 10.1145/2970398.2970420

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Abstract

We consider the problem of identifying possible companions for a user who is planning to visit a given venue. Specifically, we study the task of predicting which of the user's current friends, in a location based social network (LBSN), are most likely to be interested in joining the visit. An important underlying assumption of our model is that friendship relations can be clustered based on the kinds of interests that are shared by the friends. To identify these friendship types, we use a latent topic model, which moreover takes into account the geographic proximity of the user to the location of the proposed venue. To the best of our knowledge, our model is the first that addresses the task of recommending companions for a proposed activity. While a number of existing topic models can be adapted to make such predictions, we experimentally show that such methods are significantly outperformed by our model.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: ACM
ISBN: 978-1-4503-4497-5
Funders: ERC, Research Grant Council of the Hong Kong Special Administrative Region, Microsoft Research Asia
Date of First Compliant Deposit: 29 July 2016
Last Modified: 01 Sep 2020 11:09
URI: http://orca.cf.ac.uk/id/eprint/93259

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