Abstract
Traveling as a very popular leisure activity enjoyed by many people all over the world. Typically, people would visit the POIs that are popular or special in a city and also have desired starting POIs (e.g., POIs that are close to their hotels) and destination POIs (e.g., POIs that are near train stations or airports). However, travelers often have limited travel time and are also unfamiliar with the wide range of Points-of-Interest (POIs) in a city, so that the itinerary planning is time-consuming and challenging. In this paper, we view this kind of itinerary planning as MandatoryTour problem, which is tourists have to construct an itinerary comprising a series of POIs of a city and including as many popular or special POIs as possible within their travel time budget. We term the most popular and special POIs as mandatory POIs in our paper. For solving the presented MandatoryTour problem, we propose a genetic algorithm GAM. We compare our approach against several baselines GA, MaxM, and GreedyM by using real-world datasets from the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), which include POI visits of seven touristic cities. The experimental results show that GAM achieves better recommendation performance in terms of the mandatory POIs, POIs visited, time budget (travel time and visit duration), and profit (POI popularity).
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References
Thomee, B., et al.: YFCC100M: the new data in multimedia research. Commun. ACM 59(2), 64–73 (2016)
Hang, L., Kang, S., Jin, W., Kim, D.: Design and implementation of an optimal travel route recommender system on big data for tourists in Jeju. Processes 6(8), 133 (2018)
Ke, C., Wu, M., Ho, W., Lai, S., Huang, L.: Intelligent point-of-interest recommendation for tourism planning via density based clustering and genetic algorithm. In: PACIS (2018)
Sun, X., Wang, J., Wu, W., Liu, W.: Genetic algorithm for optimizing routing design and fleet allocation of freeway service overlapping patrol. Sustainability 10(11), 4120 (2018)
Tiwari, S., Kaushik, S.: Modeling personalized recommendations of unvisited tourist places using genetic algorithms. In: Chu, W., Kikuchi, S., Bhalla, S. (eds.) DNIS 2015. LNCS, vol. 8999, pp. 264–276. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16313-0_20
Corsar, D., Edwards, P., Nelson, J., Baillie, C., Papangelis, K., Velaga, N.: Linking open data and the crowd for real-time passenger information. J. Web Semant. 43, 18–24 (2017)
Sylejmani, K., Dorn, J., Musliu, N.: Planning the trip itinerary for tourist groups. Inf. Technol. Tourism 17(3), 275–314 (2017). https://doi.org/10.1007/s40558-017-0080-9
Lim, K., Chan, J., Karunasekera, S., Leckie, C.: Personalized itinerary recommendation with queuing time awareness. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, pp. 325–334 (2017)
Lim, K.H., Chan, J., Leckie, C., Karunasekera, S.: Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. Knowl. Inf. Syst. 54(2), 375–406 (2017). https://doi.org/10.1007/s10115-017-1056-y
Taylor, K., Lim, K., Chan, J.: Travel itinerary recommendations with must-see points-of-interest. In: Companion Proceedings of the The Web Conference 2018. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, pp. 1198–1205 (2019)
Wang, X., Leckie, C., Chan, J., Lim, K., Vaithianathan, T.: Improving personalized trip recommendation by avoiding crowds. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. Association for Computing Machinery, pp. 25–34 (2016)
Cai, G., Lee, K., Lee, I.: Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos. Expert Syst. Appl. 94, 32–40 (2018)
Zhang, Y., Tang, J.: Itinerary planning with time budget for risk-averse travelers. Eur. J. Oper. Res. 267(1), 288–303 (2018)
Binucci, C., Luca, F., Giacomo, E., Liotta, G., Montecchiani, F.: Designing the content analyzer of a travel recommender system. Expert Syst. Appl. 87, 199–208 (2017)
Laß, C., Herzog, D., Wörndl, W.: Context-aware tourist trip recommendations. In: RecTour@RecSys (2017)
Wang, H., Wang, N., Yeung, D.: Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235–1244 (2015)
Yang, C., Bai, L., Zhang, C., Yuan, Q., Han, J.: Bridging collaborative filtering and semi-supervised learning: a neural approach for POI recommendation. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1245–1254 (2017)
Al-hassan, M., Lu, H., Lu, J.: A semantic enhanced hybrid recommendation approach: a case study of e-government tourism service recommendation system. Decis. Support Syst. 72, 97–109 (2015)
Kefalas, P., Symeonidis, P., Manolopoulos, Y.: Recommendations based on a heterogeneous spatio-temporal social network. World Wide Web 21(2), 345–371 (2017). https://doi.org/10.1007/s11280-017-0454-0
Acknowledgements
This work was supported in part by the Natural Science Foundation of China (Nos. U1711263, U1811264, 61966009), and in part by the Natural Science Foundation of Guangxi Province (No. 2018GXNSFDA281045).
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Yochum, P., Chang, L., Gu, T., Zhu, M., Chen, H. (2020). A Genetic Algorithm for Travel Itinerary Recommendation with Mandatory Points-of-Interest. In: Shi, Z., Vadera, S., Chang, E. (eds) Intelligent Information Processing X. IIP 2020. IFIP Advances in Information and Communication Technology, vol 581. Springer, Cham. https://doi.org/10.1007/978-3-030-46931-3_13
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DOI: https://doi.org/10.1007/978-3-030-46931-3_13
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