Acta Informatica Pragensia 2021, 10(3), 275-288 | DOI: 10.18267/j.aip.1613185

Discovery of Points of Interest with Different Granularities for Tour Recommendation Using a City Adaptive Clustering Framework

Junjie Sun, Tomoki Kinoue, Qiang Ma ORCID...
Department of Social Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto-shi, Japan

Increasing demand for personalized tours for tourists travel in an urban area motivates more attention to points of interest (POI) and tour recommendation services. Recently, the granularity of POI has been discussed to provide more detailed information for tour planning, which supports both inside and outside routes that would improve tourists' travel experience. Such tour recommendation systems require a predefined POI database with different granularities, but existing POI discovery methods do not consider the granularity of POI well and treat all POIs as the same scale. On the other hand, the parameters also need to be tuned for different cities, which is not a trivial process. To this end, we propose a city adaptive clustering framework for discovering POIs with different granularities in this article. Our proposed method takes advantage of two clustering algorithms and is adaptive to different cities due to automatic identification of suitable parameters for different datasets. Experiments on two real-world social image datasets reveal the effectiveness of our proposed framework. Finally, the discovered POIs with two levels of granularity are successfully applied on inner and outside tour planning.

Keywords: Points of interest; Location-based social networks; Sightseeing; Social informatics; Clustering.

Received: July 31, 2021; Revised: October 18, 2021; Accepted: October 23, 2021; Prepublished online: October 23, 2021; Published: December 31, 2021  Show citation

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Sun, J., Kinoue, T., & Ma, Q. (2021). Discovery of Points of Interest with Different Granularities for Tour Recommendation Using a City Adaptive Clustering Framework. Acta Informatica Pragensia, 10(3), 275-288. doi: 10.18267/j.aip.161
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    References

    1. Ankerst, M., Breunig, M. M., Kriegel, H. P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. ACM Sigmod record, 28(2), 49-60. https://doi.org/10.1145/304181.304187 Go to original source...
    2. Bühler, T., & Hein, M. (2009). Spectral clustering based on the graph p-Laplacian. In Proceedings of the 26th Annual International Conference on Machine Learning (pp. 81-88). ACM. https://doi.org/10.1145/1553374.1553385 Go to original source...
    3. Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence, 24(5), 603-619. https://doi.org/10.1109/34.1000236 Go to original source...
    4. Crandall, D. J., Backstrom, L., Huttenlocher, D., & Kleinberg, J. (2009). Mapping the world's photos. In Proceedings of the 18th international conference on World wide web (pp. 761-770). https://doi.org/10.1145/1526709.1526812 Go to original source...
    5. Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD'96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (pp. 226-231). ACM.
    6. Fusco, S. J., Michael, K., & Michael, M. G. (2010). Using a social informatics framework to study the effects of location-based social networking on relationships between people: A review of literature. In 2010 IEEE international symposium on technology and society (pp. 157-171). IEEE. https://doi.org/10.1109/ISTAS.2010.5514641 Go to original source...
    7. Kennedy, L. S., & Naaman, M. (2008). Generating diverse and representative image search results for landmarks. In Proceedings of the 17th international conference on World Wide Web (pp. 297-306). ACM. https://doi.org/10.1145/1367497.1367539 Go to original source...
    8. Kisilevich, S., Mansmann, F., & Keim, D. (2010). P-DBSCAN: A density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. In Proceedings of the 1st international conference and exhibition on computing for geospatial research & application (pp. 1-4). ACM. https://doi.org/10.1145/1823854.1823897 Go to original source...
    9. Kuo, C. L., Chan, T. C., Fan, I., & Zipf, A. (2018). Efficient method for POI/ROI discovery using Flickr geotagged photos. ISPRS International Journal of Geo-Information, 7(3), 121. https://doi.org/10.3390/ijgi7030121 Go to original source...
    10. Laptev, D., Tikhonov, A., Serdyukov, P., & Gusev, G. (2014). Parameter-free discovery and recommendation of areas-of-interest. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 113-122). ACM. https://doi.org/10.1145/2666310.2666416 Go to original source...
    11. Lee, I., Cai, G., & Lee, K. (2014). Exploration of geo-tagged photos through data mining approaches. Expert Systems with Applications, 41(2), 397-405. https://doi.org/10.1016/j.eswa.2013.07.065 Go to original source...
    12. Li, J., Ma, Q., Asano, Y., & Yoshikawa, M. (2012). Improving Folksonomy Tag Quality of Social Image Hosting Website. In International Conference on Web-Age Information Management (pp. 264-275). Springer. https://doi.org/10.1007/978-3-642-33050-6_26 Go to original source...
    13. Liu, J., Huang, Z., Chen, L., Shen, H. T., & Yan, Z. (2012). Discovering areas of interest with geo-tagged images and check-ins. In Proceedings of the 20th ACM international conference on Multimedia (pp. 589-598). ACM. https://doi.org/10.1145/2393347.2393429 Go to original source...
    14. Masami, H. (2014). Defining Informatics: Reference Criteria in Informatics [情報学を定義する-情報学分野の参照基準] (In Japanese). Information processing [情報処理], 55(7), 734-743.
    15. Sun, J., Kinoue, T., & Ma, Q. (2020). A City Adaptive Clustering Framework for Discovering POIs with Different Granularities. In International Conference on Database and Expert Systems Applications (pp. 425-434). Springer. https://doi.org/10.1007/978-3-030-59003-1_28 Go to original source...
    16. Wang, C., Gao, Y., Gao, X., Yao, B., & Chen, G. (2018). eTour: A two-layer framework for tour recommendation with super-pois. In International Conference on Service-Oriented Computing (pp. 771-778). Springer. https://doi.org/10.1007/978-3-030-03596-9_55 Go to original source...
    17. Yang, Y., Gong, Z., & U, L. H. (2011). Identifying points of interest by self-tuning clustering. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (pp. 883-892). ACM. https://doi.org/10.1145/2009916.2010034 Go to original source...
    18. Yang, Y., Gong, Z., Li, Q., U, L. H., Cai, R., & Hao, Z. (2017). A robust noise resistant algorithm for POI identification from flickr data. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 3294-3300). IJCAI. https://doi.org/10.24963/ijcai.2017/460 Go to original source...
    19. Zhang, J., Yang, Y., Tian, Q., Zhuo, L., & Liu, X. (2017). Personalized social image recommendation method based on user-image-tag model. IEEE Transactions on Multimedia, 19(11), 2439-2449. https://doi.org/10.1109/TMM.2017.2701641 Go to original source...
    20. Zhuang, C., Ma, Q., Liang, X., & Yoshikawa, M. (2014). Anaba: An obscure sightseeing spots discovering system. In 2014 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE. https://doi.org/10.1109/ICME.2014.6890225 Go to original source...

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