research-article

TRACE: Travel Reinforcement Recommendation Based on Location-Aware Context Extraction

Authors:

Xi NiuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 4

Article No.: 65, Pages 1 - 22

https://doi.org/10.1145/3487047

Published: 08 January 2022 Publication History

Abstract

As the popularity of online travel platforms increases, users tend to make ad-hoc decisions on places to visit rather than preparing the detailed tour plans in advance. Under the situation of timeliness and uncertainty of users’ demand, how to integrate real-time context into dynamic and personalized recommendations have become a key issue in travel recommender system. In this article, by integrating the users’ historical preferences and real-time context, a location-aware recommender system called TRACE (Travel Reinforcement Recommendations Based on Location-Aware Context Extraction) is proposed. It captures users’ features based on location-aware context learning model, and makes dynamic recommendations based on reinforcement learning. Specifically, this research: (1) designs a travel reinforcing recommender system based on an Actor-Critic framework, which can dynamically track the user preference shifts and optimize the recommender system performance; (2) proposes a location-aware context learning model, which aims at extracting user context from real-time location and then calculating the impacts of nearby attractions on users’ preferences; and (3) conducts both offline and online experiments. Our proposed model achieves the best performance in both of the two experiments, which demonstrates that tracking the users’ preference shifts based on real-time location is valuable for improving the recommendation results.

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Cited By

Chen LZhu G(2025)Self-supervised contrastive learning for itinerary recommendationExpert Systems with Applications10.1016/j.eswa.2024.126246268(126246)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126246
Li CCui JLi Y(2024)Research on System Development of Spatial Clustering in Tourism RecommendationApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-08409:1Online publication date: 1-Apr-2024
https://doi.org/10.2478/amns-2024-0840
Chen LCao JLiang WYe Q(2024)Geography-aware Heterogeneous Graph Contrastive Learning for Travel RecommendationACM Transactions on Spatial Algorithms and Systems10.1145/364127710:3(1-22)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1145/3641277
Show More Cited By

Index Terms

TRACE: Travel Reinforcement Recommendation Based on Location-Aware Context Extraction
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 4

August 2022

529 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3505210

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 January 2022

Accepted: 01 September 2021

Revised: 01 June 2021

Received: 01 February 2021

Published in TKDD Volume 16, Issue 4

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Cited By

Chen LZhu G(2025)Self-supervised contrastive learning for itinerary recommendationExpert Systems with Applications10.1016/j.eswa.2024.126246268(126246)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126246
Li CCui JLi Y(2024)Research on System Development of Spatial Clustering in Tourism RecommendationApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-08409:1Online publication date: 1-Apr-2024
https://doi.org/10.2478/amns-2024-0840
Chen LCao JLiang WYe Q(2024)Geography-aware Heterogeneous Graph Contrastive Learning for Travel RecommendationACM Transactions on Spatial Algorithms and Systems10.1145/364127710:3(1-22)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1145/3641277
Lin YLiu YLin FZou LWu PZeng WChen HMiao C(2024)A Survey on Reinforcement Learning for Recommender SystemsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.328016135:10(13164-13184)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3280161
Evangeline SMoorthy A(2024)Improving Communication for People with Speech Disabilities: Exploring Context-Aware Recommendation Systems in Assistive Technology2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)10.1109/ICEC59683.2024.10837227(1-6)Online publication date: 23-Nov-2024
https://doi.org/10.1109/ICEC59683.2024.10837227
Rahin SHui BLi W(2024)Location-Aware Context Detection Based-On Behavior Sensors2024 6th International Conference on Computer Communication and the Internet (ICCCI)10.1109/ICCCI62159.2024.10674531(83-93)Online publication date: 14-Jun-2024
https://doi.org/10.1109/ICCCI62159.2024.10674531
Mateos PBellogín A(2024)A systematic literature review of recent advances on context-aware recommender systemsArtificial Intelligence Review10.1007/s10462-024-10939-458:1Online publication date: 16-Nov-2024
https://doi.org/10.1007/s10462-024-10939-4
Fu ZNiu X(2023)Modeling Users’ Curiosity in Recommender SystemsACM Transactions on Knowledge Discovery from Data10.1145/361759818:1(1-23)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3617598
Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 26-Aug-2023
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Amin SUddin MAlarood AMashwani WAlzahrani AAlzahrani A(2023)Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement LearningIEEE Access10.1109/ACCESS.2023.330558411(89769-89790)Online publication date: 2023
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