Abstract
The effective suggestion of venues that are appropriate for a user to visit is a challenging problem, as the appropriateness of a venue can depend on particular contextual aspects, such as the duration of the user’s visit, or the composition of the user’s travelling group (e.g. alone, with friends, or with family). This paper proposes a supervised approach that predicts appropriateness of venues to particular contextual aspects, by leveraging user-generated data in Location-Based Social Networks (LBSNs) such as Foursquare. Our approach learns a binary classifier for each dimension of three considered contextual aspects. A set of discriminative features are extracted from the comments, photos and website of venues. Using a dataset from the TREC 2015 Contextual Suggestion track, supplemented by venue annotations generated by crowdsourcing, we conduct a comprehensive experimental study to identify the set of features appropriate for our problem and to evaluate the effectiveness of our proposed approach. Our results demonstrate both the accuracy of our classification approach in predicting suitable contextual aspects for a venue, and its effectiveness at making better venue recommendations than the best performing system in TREC 2015.
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Notes
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Our crowdsourced venue annotations are freely available from http://dx.doi.org/10.5525/gla.researchdata.325.
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Manotumruksa, J., Macdonald, C., Ounis, I. (2016). Predicting Contextually Appropriate Venues in Location-Based Social Networks. In: Fuhr, N., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2016. Lecture Notes in Computer Science(), vol 9822. Springer, Cham. https://doi.org/10.1007/978-3-319-44564-9_8
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DOI: https://doi.org/10.1007/978-3-319-44564-9_8
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