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On-device Deep Learning Location Category Inference Model

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Artificial Intelligence and Machine Learning (BNAIC/Benelearn 2022)

Abstract

We define Location Category Inference (LCI) as a task of predicting the category of a visited venue, such as bar, restaurant or university, given user location GPS coordinates and a set of venue candidates. LCI is an essential part of the hyper-personalization systems as its output provides deep insights into user lifestyle (has children, owns a dog) and behavioral patterns (regularly exercises, visits museums). Due to such factors as signal obstruction, especially in urban canyons, the GPS positioning is inaccurate. The noise in the GPS signal makes the problem of LCI challenging and requires researchers to explore models that incorporate additional information such as the time of day, duration of stay or user lifestyle in order to overcome the noise-induced errors. In this paper we propose an embeddable on-device LCI model which fuses spatial and temporal features. We discuss how initial clustering of locations helps limiting the GPS noise. Then, we propose a multi-modal architecture that incorporates socio-cultural information on when and for how long people typically visit venues of different categories. Finally, we compare our model with one nearest neighbor, a simple fully connected neural network and a random forest model and show that the multi-modal neural network achieves f1 score of 73.2% which is 6.6% better than the best of benchmark models. Our model outperforms benchmark models while being almost 180 times smaller in size at around 1.9Mb.

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References

  1. Shaw, B., Shea, J., Sinha, S., Hogue, A.: Learning to rank for spatiotemporal search. In: WSDM 2013 (2013). https://doi.org/10.1145/2433396.2433485

  2. Alsudais, A., Leroy, G., Corso, A.: We know where you are tweeting from: assigning a type of place to tweets using natural language processing and random forests. In: 2014 IEEE International Congress on Big Data, pp. 594–600 (2014). https://doi.org/10.1109/BigData.Congress.2014.91. ISSN 2379-7703

  3. Sabatelli, M., Osmani, V., Mayora, O., Gruenerbl, A., Lukowicz, P.: Correlation of significant places with self-reported state of bipolar disorder patients. In: 2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH), pp. 116–119 (2014). https://doi.org/10.1109/MOBIHEALTH.2014.7015923

  4. Bao, J., Zheng, Yu., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: a survey. GeoInformatica 19(3), 525–565 (2015). https://doi.org/10.1007/s10707-014-0220-8

    Article  Google Scholar 

  5. McKenzie, G., Janowicz, K.: Where is also about time: a location-distortion model to improve reverse geocoding using behavior-driven temporal semantic signatures. Comput. Environ. Urban Syst. 54, 1–13 (2015). https://doi.org/10.1016/j.compenvurbsys.2015.05.003

    Article  Google Scholar 

  6. Yang, D., Zhang, D., Chen, L., Qu, B.: NationTelescope: monitoring and visualizing large-scale collective behavior in LBSNs. J. Netw. Comput. Appl. 55, 170–180 (2015). https://doi.org/10.1016/j.jnca.2015.05.010. ISSN 1084-8045

    Article  Google Scholar 

  7. Yang, D., Zhang, D., Qu, B.: Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Trans. Intell. Syst. Technol. 7(3), 30:1–30:23 (2016). https://doi.org/10.1145/2814575. ISSN 2157-6904

  8. Pang, J., Zhang, Y.: DeepCity: a feature learning framework for mining location check-ins. In: Eleventh International AAAI Conference on Web and Social Media (2017)

    Google Scholar 

  9. He, J., Li, X., Liao, L., Cheung, W.K.: Personalized next Point-of-Interest Recommendation via Latent Behavior Patterns Inference (2018). arXiv:1805.06316

  10. Keßler, C., McKenzie, G.: A geoprivacy manifesto. Trans. GIS 22(1), 3–19 (2018). https://doi.org/10.1111/tgis.12305. ISSN 1467-9671

    Article  Google Scholar 

  11. Duan, Y., Lu, W., Xing, W., Bao, P., Wei, X.: PBEM: a pattern-based embedding model for user location category prediction. In: 2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU), pp. 1–6 (2019). https://doi.org/10.23919/ICMU48249.2019.9006662

  12. Kim, Y.M., Song, H.Y.: Analysis of relationship between personal factors and visiting places using random forest technique. In: 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 725–732 (2019). https://doi.org/10.15439/2019F318. ISSN 2300-5963

  13. Wongvibulsin, S., Martin, S., Saria, S., Zeger, S., Murphy, S.: An individualized, data-driven digital approach for precision behavior change. Am. J. Lifestyle Med. 14, 155982761984348 (2019). https://doi.org/10.1177/1559827619843489

    Article  Google Scholar 

  14. Yi, J., Lei, Q., Gifford, W.M., Liu, J., Yan, J., Zhou, B.: Fast unsupervised location category inference from highly inaccurate mobility data. In: Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), Proceedings, pp. 55–63. Society for Industrial and Applied Mathematics (2019). https://doi.org/10.1137/1.9781611975673.7

  15. Andrade, T., Cancela, B., Gama, J.: Mining human mobility data to discover locations and habits. In: Cellier, P., Driessens, K. (eds.) ECML PKDD 2019. CCIS, vol. 1168, pp. 390–401. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43887-6_32

    Chapter  Google Scholar 

  16. Angmo, R., Aggarwal, N., Mangat, V., Lal, A., Kaur, S.: An improved clustering approach for identifying significant locations from spatio-temporal data. Wireless Pers. Commun. 121(1), 985–1009 (2021). https://doi.org/10.1007/s11277-021-08668-w

    Article  Google Scholar 

  17. De Maio, C., Gallo, M., Hao, F., Yang, E.: Who and where: context-aware advertisement recommendation on Twitter. Soft. Comput. 25(1), 379–387 (2020). https://doi.org/10.1007/s00500-020-05147-5

    Article  Google Scholar 

  18. Islam, M.A., Mohammad, M.M., Das, S.S.S., Ali, M.E.: A survey on deep learning based point-of-interest (POI) recommendations. Neurocomputing 472, 306–325 (2022). https://doi.org/10.1016/j.neucom.2021.05.114. ISSN 0925-2312

    Article  Google Scholar 

  19. McKenzie, G., Romm, D., Zhang, H., Brunila, M.: PrivyTo: a privacy-preserving location-sharing platform. Trans. GIS 26(4), 1703–1717 (2022). https://doi.org/10.1111/tgis.12924

  20. Zhang, H., McKenzie, G.: Rehumanize geoprivacy: from disclosure control to human perception. GeoJournal 88(1), 189–208 (2022). https://doi.org/10.1007/s10708-022-10598-4. ISSN 1572-9893

    Article  Google Scholar 

  21. Sentiance journeys app for android. https://play.google.com/store/apps/details?id=com.sentiance.journeys. Accessed 01 Apr 2023

  22. Sentiance journeys app for IoS. https://apps.apple.com/be/app/journeys-by-sentiance/id984087229. Accessed 01 Apr 2023

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Correspondence to Gadzhi Musaev .

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A Appendix

A Appendix

Table 3. Full list of location categories.
Table 4. Comparison of quantization techniques. We report the size of the data-only buffers in bytes, f1 score of the model and the mean run time of inference in milliseconds

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Musaev, G., Mets, K., Tamošiūnas, R., Uvarov, V., De Schepper, T., Hellinckx, P. (2023). On-device Deep Learning Location Category Inference Model. In: Calders, T., Vens, C., Lijffijt, J., Goethals, B. (eds) Artificial Intelligence and Machine Learning. BNAIC/Benelearn 2022. Communications in Computer and Information Science, vol 1805. Springer, Cham. https://doi.org/10.1007/978-3-031-39144-6_7

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  • DOI: https://doi.org/10.1007/978-3-031-39144-6_7

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