Abstract
Location-specific community authored reviews are useful resource for discovering location-specific activities and developing various location-aware activity recommendation applications. Existing works on activity discovery have mostly utilized body-worn sensors, images or human GPS traces and discovered generalized activities that do not convey any location-specific knowledge. Moreover, many of the discovered activities are irrelevant and redundant and hence, significantly affect the performance of a location-aware activity recommender system. In this paper, we propose a three-phase Discover-Filer-Merge solution, namely ActMiner, to infer the location-specific relevant and non-redundant activities from community-authored reviews. The proposed solution uses Dependency-aware, Category-aware and Sense-aware approaches in three sequential phases to accomplish its objective. Experimental results on two real-world data sets show that the accuracy and correctness of ActMiner are better than the existing approaches.
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Mazumder, S., Patel, D., Mehta, S. (2014). ActMiner: Discovering Location-Specific Activities from Community-Authored Reviews. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2014. Lecture Notes in Computer Science, vol 8646. Springer, Cham. https://doi.org/10.1007/978-3-319-10160-6_30
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DOI: https://doi.org/10.1007/978-3-319-10160-6_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10159-0
Online ISBN: 978-3-319-10160-6
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