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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References
Yelp. An Introduction to Yelp: Metrics as of June 30, 2013 (2013), http://www.yelp.co.nz/html/pdf/Snapshot_2013_Q2_en.pdf
Dearman, D., Truong, K.N.: Identifying the activities supported by locations with community-authored content. ACM UbiComp, 23–32 (2010)
Ku, W.S., Zimmermann, R., Wang, H.: Location-based spatial queries with data sharing in wireless broadcast environments. In: IEEE ICDE, pp. 1355–1359 (2007)
ConceptNet 5, http://conceptnet5.media.mit.edu/
Lara, O., Labrador, M.: A survey on human activity recognition using wearable sensors. IEEE Communications Surveys and Tutorials 15(3), 1192–1209 (2012)
Pawar, T., Chaudhuri, S., Duttagupta, S.P.: Body movement activity recognition for ambulatory cardiac monitoring. IEEE Tran. on Biomedical Eng. 54(5), 874–882 (2007)
Zheng, K., Shang, S., Yuan, N.J., Yang, Y.: Towards Efficient Search for Activity Trajectories. In: IEEE ICDE (2013)
Furletti, B., Cintia, P., Renso, C., Spinsanti, L.: Inferring human activities from GPS tracks. In: ACM SIGKDD International Workshop on Urban Computing, p. 5 (2013)
Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with GPS history data. In: ACM WWW, pp. 1029–1038 (2010)
Apache OpenNLP Developer Documentation, https://opennlp.apache.org/
The Stanford Parser: A statistical parser, http://nlp.stanford.edu/software/lex-parser.shtml
De Marneffe, M.C., Manning, C.D.: Stanford typed dependencies manual (2008), http://nlp.stanford.edu/software/dependenciesmanual.pdf
Stanford Log-linear Part-Of-Speech Tagger, http://nlp.stanford.edu/downloads/tagger.shtml
WordNet, http://wordnet.princeton.edu/
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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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