research-article

Learning from Hometown and Current City: Cross-city POI Recommendation via Interest Drift and Transfer Learning

Authors:

Hui GaoAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 4

Article No.: 131, Pages 1 - 28

https://doi.org/10.1145/3369822

Published: 14 September 2020 Publication History

Abstract

With more and more frequent population movement between different cities, like users' travel or business trip, recommending personalized cross-city Point-of-Interests (POIs) for these users has become an important scenario of POI recommendation tasks. However, traditional models degrade significantly due to sparsity problem because travelers only have limited visiting behaviors. Through a detailed analysis of real-world check-data, we observe 1) the phenomenon of travelers' interest drift and transfer co-exist between hometown and current city; 2) differences between popular POIs among locals and travelers. Motivated by this, we propose a POI Recommendation framework with User Interest Drift and Transfer (PR-UIDT), which jointly considers above two factors when designing user and POI latent vector. In this framework, user vector is divided into a city-independent part and another city-dependent part, and POI is represented as two independent vectors for locals and travelers, respectively. To evaluate the proposed framework, we implement it with a square error based matrix factorization model and a ranking error based matrix factorization model, respectively, and conduct extensive experiments on three real-world datasets. The experiment results demonstrate the superiority of PR-UIDT framework, with a relative improvement of 0.4% ~ 20.5% over several state-of-the-art baselines, as well as the practicality of applying this framework to real-world applications and multi-city scenarios. Further qualitative analysis confirms both the plausibility and validity of combining user interest transfer and drift into cross-city POI recommendation.

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Cited By

Acharya MMohbey K(2025)Time-aware cross-domain point-of-interest recommendation in social networksEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109630139(109630)Online publication date: Jan-2025
https://doi.org/10.1016/j.engappai.2024.109630
Sun KLi CQian T(2024)City Matters! A Dual-Target Cross-City Sequential POI Recommendation ModelACM Transactions on Information Systems10.1145/366428442:6(1-27)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3664284
Gilman EBugiotti FKhalid AMehmood HKostakos PTuovinen LYlipulli JSu XFerreira D(2024)Addressing Data Challenges to Drive the Transformation of Smart CitiesACM Transactions on Intelligent Systems and Technology10.1145/366348215:5(1-65)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.1145/3663482
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Index Terms

Learning from Hometown and Current City: Cross-city POI Recommendation via Interest Drift and Transfer Learning
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering
    2. Spatial-temporal systems
      1. Location based services

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 4

December 2019

873 pages

EISSN:2474-9567

DOI:10.1145/3375704

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Copyright © 2019 ACM.

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Publication History

Published: 14 September 2020

Published in IMWUT Volume 3, Issue 4

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the National Key Research and Development Program of China
research fund of Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology
the National Nature Science Foundation of China
Beijing Natural Science Foundation
Beijing National Research Center for Information Science and Technology

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Cited By

Acharya MMohbey K(2025)Time-aware cross-domain point-of-interest recommendation in social networksEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109630139(109630)Online publication date: Jan-2025
https://doi.org/10.1016/j.engappai.2024.109630
Sun KLi CQian T(2024)City Matters! A Dual-Target Cross-City Sequential POI Recommendation ModelACM Transactions on Information Systems10.1145/366428442:6(1-27)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3664284
Gilman EBugiotti FKhalid AMehmood HKostakos PTuovinen LYlipulli JSu XFerreira D(2024)Addressing Data Challenges to Drive the Transformation of Smart CitiesACM Transactions on Intelligent Systems and Technology10.1145/366348215:5(1-65)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.1145/3663482
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
Jin JSong YKan DZhang BLyu YZhang JLu H(2024)Learning context-aware region similarity with effective spatial normalization over Point-of-Interest dataInformation Processing & Management10.1016/j.ipm.2024.10367361:3(103673)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2024.103673
Yang TGao YHuang ZLiu Y(2023)UPTDNetInternational Journal of Intelligent Systems10.1155/2023/90915702023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/9091570
Ni XXiong FPan SWu JWang LChen H(2023)Community Preserving Social Recommendation with Cyclic Transfer LearningACM Transactions on Information Systems10.1145/363111542:3(1-36)Online publication date: 29-Dec-2023
https://dl.acm.org/doi/10.1145/3631115
Wang YZhao DRen YZhang DMa H(2023)SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New CityACM Transactions on Knowledge Discovery from Data10.1145/356557717:4(1-25)Online publication date: 24-Feb-2023
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Li DGong Z(2023)A Deep Neural Network for Crossing-City POI Recommendations : (Extended Abstract)2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00352(3851-3852)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00352
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
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