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Probabilistic Social Sequential Model for Tour Recommendation

Authors:

Vineeth Rakesh,

Niranjan Jadhav,

Alexander Kotov,

Chandan K. ReddyAuthors Info & Claims

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

Pages 631 - 640

https://doi.org/10.1145/3018661.3018711

Published: 02 February 2017 Publication History

Abstract

The pervasive growth of location-based services such as Foursquare and Yelp has enabled researchers to incorpo- rate better personalization into recommendation models by leveraging the geo-temporal breadcrumbs left by a plethora of travelers. In this paper, we explore Travel path recommendation, which is one of the applications of intelligent urban navigation that aims in recommending sequence of point of interest (POIs) to tourists. Currently, travelers rely on a tedious and time-consuming process of searching the web, browsing through websites such as Trip Advisor, and reading travel blogs to compile an itinerary. On the other hand, people who do not plan ahead of their trip find it extremely difficult to do this in real-time since there are no automated systems that can provide personalized itinerary for travelers. To tackle this problem, we propose a tour recommendation model that uses a probabilistic generative framework to incorporate user's categorical preference, influence from their social circle, the dynamic travel transitions (or patterns) and the popularity of venues to recommend sequence of POIs for tourists. Through comprehensive experiments over a rich dataset of travel patterns from Foursquare, we show that our model is capable of outperforming the state-of-the-art probabilistic tour recommendation model by providing contextual and meaningful recommendation for travelers.

References

[1]

Berjani, B., and Strufe, T. A recommendation system for spots in location-based online social networks. In Proceedings of the 4th Workshop on Social Network Systems (2011), ACM.

Digital Library

[2]

Blei, D. M., Ng, A. Y., and Jordan, M. I. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993--1022.

[3]

Cheng, C., Yang, H., King, I., and Lyu, M. R. Fused matrix factorization with geographical and social in uence in location-based social networks. In Twenty-Sixth AAAI Conference on Arti cial Intelligence (2012).

[4]

Cheng, C., Yang, H., Lyu, M. R., and King, I. Where you like to go next: successive point-of-interest recommendation. In Proceedings of the Twenty-Third International Joint Conference on Arti cial Intelligence (2013), AAAI Press, pp. 2605--2611.

[5]

Cheng, Z., Caverlee, J., Lee, K., and Sui, D. Z. Exploring millions of footprints in location sharing services. ICWSM 2011 (2011), 81--88.

[6]

Gao, H., Tang, J., Hu, X., and Liu, H. Exploring temporal e ects for location recommendation on location-based social networks. In Proceedings of the 7th ACM conference on Recommender systems (2013), ACM, pp. 93--100.

Digital Library

[7]

Ge, Y., Liu, Q., Xiong, H., Tuzhilin, A., and Chen, J. Cost-aware travel tour recommendation. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (2011), ACM, pp. 983--991.

Digital Library

[8]

Gionis, A., Lappas, T., Pelechrinis, K., and Terzi, E. Customized tour recommendations in urban areas. In Proceedings of the 7th ACM international conference on Web search and data mining (2014), ACM, pp. 313--322.

Digital Library

[9]

Hsieh, H.-P., and Li, C.-T. Mining and planning time-aware routes from check-in data. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (2014), ACM, pp. 481--490.

Digital Library

[10]

Hu, B., and Ester, M. Spatial topic modeling in online social media for location recommendation. In Proceedings of the 7th ACM conference on Recommender systems (2013), ACM, pp. 25--32.

Digital Library

[11]

Kotov, A., and Agichtein, E. The importance of being socially-savvy: quantifying the in uence of social networks on microblog retrieval. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management (2013), ACM, pp. 1905--1908.

Digital Library

[12]

Kotov, A., Rakesh, V., Agichtein, E., and Reddy, C. K. Geographical latent variable models for microblog retrieval. In Proceedings of the 37th European Conference on Information Retrieval (2015), ACM, pp. 635--647.

[13]

Kotov, A., Wang, Y., and Agichtein, E. Leveraging geographical metadata to improve search over social media. In Proceedings of the 22nd International Conference on World Wide Web (2013), ACM, pp. 151--152.

Digital Library

[14]

Kurashima, T., Iwata, T., Irie, G., and Fujimura, K. Travel route recommendation using geotags in photo sharing sites. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (2010), ACM, pp. 579--588.

Digital Library

[15]

Le Falher, G., Gionis, A., and Mathioudakis, M. Where is the soho of rome? measures and algorithms for finding similar neighborhoods in cities. In AAAI Conference on Web and Social Media (2015).

[16]

Lian, D., Ge, Y., Zhang, F., Yuan, N. J., Xie, X., Zhou, T., and Rui, Y. Content-aware collaborative ltering for location recommendation based on human mobility data. In Data Mining (ICDM), 2015 IEEE International Conference on (2015), IEEE, pp. 261--270.

Digital Library

[17]

Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., and Rui, Y. Geomf: Joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (2014), ACM, pp. 831--840.

Digital Library

[18]

Liu, B., and Xiong, H. Point-of-interest recommendation in location based social networks with topic and location awareness. In SDM (2013), vol. 13, SIAM, pp. 396--404.

[19]

Liu, Q., Ge, Y., Li, Z., Chen, E., and Xiong, H. Personalized travel package recommendation. In Data Mining (ICDM), 2011 IEEE 11th International Conference on (2011), IEEE, pp. 407--416.

Digital Library

[20]

Liu, X., Liu, Y., Aberer, K., and Miao, C. Personalized point-of-interest recommendation by mining users' preference transition. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management (2013), ACM, pp. 733--738.

Digital Library

[21]

Park, M.-H., Hong, J.-H., and Cho, S.-B. Location-based recommendation system using bayesian user's preference model in mobile devices. In Ubiquitous Intelligence and Computing. Springer, 2007, pp. 1130--1139.

[22]

Popescul, A., Pennock, D. M., and Lawrence, S. Probabilistic models for uni ed collaborative and content-based recommendation in sparse-data environments. In Proceedings of the Seventeenth conference on Uncertainty in arti cial intelligence (2001), Morgan Kaufmann Publishers Inc., pp. 437--444.

Digital Library

[23]

Rakesh, V., Lee, W.-C., and Reddy, C. K. Probabilistic group recommendation model for crowdfunding domains. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (2016), ACM, pp. 257--266.

Digital Library

[24]

Ramage, D., Hall, D., Nallapati, R., and Manning, C. D. Labeled lda: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1- (2009), Association for Computational Linguistics, pp. 248--256.

[25]

Roy, S. B., Das, G., Amer-Yahia, S., and Yu, C. Interactive itinerary planning. In IEEE 27th International Conference on Data Engineering (2011), IEEE, pp. 15--26.

[26]

Wallach, H. M. Topic modeling: beyond bag-of-words. In Proceedings of the 23rd International Conference on Machine Learning (2006), ACM, pp. 977--984.

Digital Library

[27]

Wang, W., Yin, H., Sadiq, S., Chen, L., Xie, M., and Zhou, X. Spore: A sequential personalized spatial item recommender system. In IEEE 32nd International Conference on Data Engineering, ICDE, (2016).

[28]

Wen, Y.-T., Cho, K.-J., Peng, W.-C., Yeo, J., and Hwang, S.-w. Kstr: Keyword-aware skyline travel route recommendation. In Data Mining (ICDM), IEEE International Conference on (2015), IEEE, pp. 449--458.

[29]

Yang, D., Zhang, D., Yu, Z., and Wang, Z. A sentiment-enhanced personalized location recommendation system. In Proceedings of the 24th ACM Conference on Hypertext and Social Media (2013), ACM, pp. 119--128.

Digital Library

[30]

Yang, Z., Kotov, A., Mohan, A., and Lu, S. Parametric and non-parametric user-aware sentiment topic models. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (2015), ACM, pp. 413--422.

Digital Library

[31]

Ye, M., Janowicz, K., Mülligann, C., and Lee, W.-C. What you are is when you are: the temporal dimension of feature types in location-based social networks. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (2011), ACM, pp. 102--111.

Digital Library

[32]

Ye, M., Yin, P., Lee, W.-C., and Lee, D.-L. Exploiting geographical in uence for collaborative point-of-interest recommendation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (2011), ACM, pp. 325--334.

Digital Library

[33]

Yin, H., Sun, Y., Cui, B., Hu, Z., and Chen, L. Lcars: a location-content-aware recommender system. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (2013), ACM, pp. 221--229.

Digital Library

[34]

Yin, Z., Cao, L., Han, J., Zhai, C., and Huang, T. Geographical topic discovery and comparison. In Proceedings of the 20th international conference on World wide web (2011), ACM, pp. 247--256.

Digital Library

[35]

Zhang, C., Liang, H., Wang, K., and Sun, J. Personalized trip recommendation with poi availability and uncertain traveling time. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (2015), ACM, pp. 911--920.

Digital Library

[36]

Zhang, W., and Wang, J. Location and time aware social collaborative retrieval for new successive point-of-interest recommendation. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (2015), ACM, pp. 1221--1230.

Digital Library

[37]

Zhao, S., Zhao, T., Yang, H., Lyu, M. R., and King, I. Stellar: Spatial-temporal latent ranking for successive point-of-interest recommendation. In Thirtieth AAAI Conference on Arti cial Intelligence (2016).

[38]

Zheng, V. W., Zheng, Y., Xie, X., and Yang, Q. Collaborative location and activity recommendations with gps history data. In Proceedings of the 19th International Conference on World Wide Web (2010), ACM, pp. 1029--1038.

Digital Library

[39]

Zheng, Y., Zhang, L., Xie, X., and Ma, W.-Y. Mining interesting locations and travel sequences from gps trajectories. In Proceedings of the 18th international conference on World wide web (2009), ACM, pp. 791--800.

Digital Library

Cited By

Kuo AChen HKu W(2023)BERT-Trip: Effective and Scalable Trip Representation using Attentive Contrast Learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00053(612-623)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00053
Hong MChung NKoo CKoh S(2023)TPEDTR: temporal preference embedding-based deep tourism recommendation with card transaction dataInternational Journal of Data Science and Analytics10.1007/s41060-022-00380-716:2(147-162)Online publication date: 19-Jan-2023
https://doi.org/10.1007/s41060-022-00380-7
Zhao JFang JChao PNing BZhang R(2023)GC-TripRec: Graph contextualized generative network with adversarial learning for trip recommendationWorld Wide Web10.1007/s11280-022-01127-x26:5(2291-2310)Online publication date: 13-Feb-2023
https://doi.org/10.1007/s11280-022-01127-x
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Index Terms

Probabilistic Social Sequential Model for Tour Recommendation
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

February 2017

868 pages

ISBN:9781450346757

DOI:10.1145/3018661

General Chairs:
Maarten de Rijke
University of Amsterdam
,
Milad Shokouhi
Microsoft
,
Program Chairs:
Andrew Tomkins
Google
,
Min Zhang
Tsinghua University

Copyright © 2017 ACM.

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Published: 02 February 2017

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National Science Foundation

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WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining

February 6 - 10, 2017

Cambridge, United Kingdom

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WSDM '17 Paper Acceptance Rate 80 of 505 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Kuo AChen HKu W(2023)BERT-Trip: Effective and Scalable Trip Representation using Attentive Contrast Learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00053(612-623)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00053
Hong MChung NKoo CKoh S(2023)TPEDTR: temporal preference embedding-based deep tourism recommendation with card transaction dataInternational Journal of Data Science and Analytics10.1007/s41060-022-00380-716:2(147-162)Online publication date: 19-Jan-2023
https://doi.org/10.1007/s41060-022-00380-7
Zhao JFang JChao PNing BZhang R(2023)GC-TripRec: Graph contextualized generative network with adversarial learning for trip recommendationWorld Wide Web10.1007/s11280-022-01127-x26:5(2291-2310)Online publication date: 13-Feb-2023
https://doi.org/10.1007/s11280-022-01127-x
Abbas RAmran GAbdulraheem AHussain IGhoniem REwees A(2022)A Fortunate Refining Trip Recommendation ModelElectronics10.3390/electronics1115245911:15(2459)Online publication date: 7-Aug-2022
https://doi.org/10.3390/electronics11152459
Gao YGao XLi XYao BChen G(2022)An Embedded GRASP-VNS based Two-Layer Framework for Tour RecommendationIEEE Transactions on Services Computing10.1109/TSC.2019.296302615:2(847-859)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TSC.2019.2963026
Gao QWang WZhang KYang XMiao CLi T(2022)Self-supervised representation learning for trip recommendationKnowledge-Based Systems10.1016/j.knosys.2022.108791247:COnline publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.108791
Zeng ZGao NXue CHe YGuo X(2021)Learning from Audience Interaction: Multi-Instance Multi-Label Topic Model for Video Shots Annotating2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD49262.2021.9437805(1075-1080)Online publication date: 5-May-2021
https://doi.org/10.1109/CSCWD49262.2021.9437805
Werneck HSilva NViana MPereira AMourão FRocha L(2021)Points of Interest recommendations: Methods, evaluation, and future directionsInformation Systems10.1016/j.is.2021.101789101(101789)Online publication date: Nov-2021
https://doi.org/10.1016/j.is.2021.101789
Stamatelatos GDrosatos GGyftopoulos SBriola HEfraimidis P(2021)Point-of-interest lists and their potential in recommendation systemsInformation Technology & Tourism10.1007/s40558-021-00195-523:2(209-239)Online publication date: 1-Feb-2021
https://doi.org/10.1007/s40558-021-00195-5
van Schaik SMasthoff JWibowo A(2020)Package recommender systems: A systematic reviewIntelligent Decision Technologies10.3233/IDT-19014013:4(435-452)Online publication date: 10-Feb-2020
https://doi.org/10.3233/IDT-190140
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