research-article

CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users

Authors:

Hui XiongAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1174 - 1184

https://doi.org/10.1145/3477495.3531949

Published: 07 July 2022 Publication History

Abstract

Pre-travel out-of-town recommendation aims to recommend Point-of-Interests (POIs) to the users who plan to travel out of their hometown in the near future yet have not decided where to go, i.e., their destination regions and POIs both remain unknown. It is a non-trivial task since the searching space is vast, which may lead to distinct travel experiences in different out-of-town regions and eventually confuse decision-making. Besides, users' out-of-town travel behaviors are affected not only by their personalized preferences but heavily by others' travel behaviors. To this end, we propose a Crowd-Aware Pre-Travel Out-of-town Recommendation framework (CAPTOR) consisting of two major modules: spatial-affined conditional random field (SA-CRF) and crowd behavior memory network (CBMN). Specifically, SA-CRF captures the spatial affinity among POIs while preserving the inherent information of POIs. Then, CBMN is proposed to maintain the crowd travel behaviors w.r.t. each region through three affiliated blocks reading and writing the memory adaptively. We devise the elaborated metric space with a dynamic mapping mechanism, where the users and POIs are distinguishable both inherently and geographically. Extensive experiments on two real-world nationwide datasets validate the effectiveness of CAPTOR against the pre-travel out-of-town recommendation task.

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Cited By

Wei NLi YWu YChen XGuo J(2024)Check-In Heterogeneous Hypergraph and Personalized Preference Transfers for Cross-City POI Recommendation MethodElectronics10.3390/electronics1324495413:24(4954)Online publication date: 16-Dec-2024
https://doi.org/10.3390/electronics13244954
Liu YShen GCui CZhao ZHan XDu JZhao XKong XLarson K(2024)KDDCProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/244(2207-2215)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/244
Sun YJi YZhu HZhuang FHe QXiong H(2024)Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement LearningACM Transactions on Information Systems10.1145/370499843:2(1-35)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3704998
Show More Cited By

Index Terms

CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
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Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
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J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Published: 07 July 2022

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Natural Science Foundation of China
Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone
Shanghai Youth Science and Technology Talents Sailing Program

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Cited By

Wei NLi YWu YChen XGuo J(2024)Check-In Heterogeneous Hypergraph and Personalized Preference Transfers for Cross-City POI Recommendation MethodElectronics10.3390/electronics1324495413:24(4954)Online publication date: 16-Dec-2024
https://doi.org/10.3390/electronics13244954
Liu YShen GCui CZhao ZHan XDu JZhao XKong XLarson K(2024)KDDCProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/244(2207-2215)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/244
Sun YJi YZhu HZhuang FHe QXiong H(2024)Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement LearningACM Transactions on Information Systems10.1145/370499843:2(1-35)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3704998
Xu SGuan DSerra ESpezzano F(2024)CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature MatchingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679893(4148-4152)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679893
Wang XChen HLiu Y(2024)Learning place representations from spatial interactionsInternational Journal of Geographical Information Science10.1080/13658816.2024.233290838:6(1065-1090)Online publication date: 27-Mar-2024
https://doi.org/10.1080/13658816.2024.2332908
Tsubouchi RHara TYonekawa KHaruta S(2023)Unvisited Out-Of-Town POI Recommendation with Simultaneous Learning of Multiple Regions2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386127(915-924)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386127

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