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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.

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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
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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

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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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