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research-article

Mapping user interest into hyper-spherical space: : A novel POI recommendation method

Authors:

Yingxue MaAuthors Info & Claims

Volume 60, Issue 2

https://doi.org/10.1016/j.ipm.2022.103169

Published: 01 March 2023 Publication History

Highlights

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A hyper-spherical interest model is proposed to improve recommendation quality.

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High-dimensional spatial model helps recommender well describing users using POIs.

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Graph representation learning helps recommender system better characterize user personality.

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A series of experiments verified the effectiveness and robustness of the model.

Abstract

Point-of-interest (POI) recommendation helps users quickly filter out irrelevant POI by considering the spatio-temporal factor. In this paper, we address the problem of check-in preference modeling in POI recommendation, and propose a novel POI recommendation method that depicts user preference by constructing unique hypersphere interest model for each user. Different from existing works, we have done three innovative work. (1) We build a check-in graph and adopt DeepWalk algorithm to learn POI embedding, further aggregating them to obtain a hypersphere interest space with an interest center and interest radius. (2) We established a stacked neural network module by a bidirectional LSTM, a self-attention and a memory network, to grasp memory features contained in check-in histories. (3) We proposed a novel candidate POI filter method that updates ranking score by evaluating the Euclidean distance between the vectors of candidate POI and interest center. We evaluate the performance of our method on the four real-world check-in datasets constructed from Foursquare. The comparison between our method and six baselines demonstrates the outstanding performance on various measurements. Compared to the best baseline method, our method achieves about 50% performance improvement on NDCG. In terms of MRR, Precision and Recall, our method achieves about 37%, 21% and 9% performance improvement over the best baseline method. Further ablation experiments verified the importance and effectiveness of the hypersphere interest model, as removing this component caused significant performance degradation.

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Cited By

Zhang HGan M(2025)A self-supervised graph-learning method for reliable-relation identification in social recommendationWorld Wide Web10.1007/s11280-025-01330-628:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11280-025-01330-6
Sang CYang YZhang YLiao S(2025)A user preference knowledge graph incorporating spatio-temporal transfer features for next POI recommendationApplied Intelligence10.1007/s10489-025-06290-y55:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-025-06290-y
Yao YZhan HNoorian AHazratifard M(2025)Enhancing POI recommendations on social media: a sequential approach incorporating LSTM and user feedbackComputing10.1007/s00607-024-01385-9107:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s00607-024-01385-9
Show More Cited By

Index Terms

Mapping user interest into hyper-spherical space: A novel POI recommendation method
1. Human-centered computing
2. Information systems

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

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Published In

cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 60, Issue 2

Mar 2023

1443 pages

ISSN:0306-4573

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2023

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Cited By

Zhang HGan M(2025)A self-supervised graph-learning method for reliable-relation identification in social recommendationWorld Wide Web10.1007/s11280-025-01330-628:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11280-025-01330-6
Sang CYang YZhang YLiao S(2025)A user preference knowledge graph incorporating spatio-temporal transfer features for next POI recommendationApplied Intelligence10.1007/s10489-025-06290-y55:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-025-06290-y
Yao YZhan HNoorian AHazratifard M(2025)Enhancing POI recommendations on social media: a sequential approach incorporating LSTM and user feedbackComputing10.1007/s00607-024-01385-9107:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s00607-024-01385-9
Zhong LZeng JWang ZZhou WWen J(2024)SCFLInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10385261:6Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103852
Liu JTian XTong HXie CRuan TCong LWu BWang H(2024)Enhancing Chinese abbreviation prediction with LLM generation and contrastive evaluationInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10376861:4Online publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103768
Gan MWang CYi LGu H(2024)Exploiting dynamic social feedback for session-based recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10363261:3Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103632
Li SGan MXu J(2024)SocialCU: integrating commonalities and uniqueness of users and items for social recommendationWorld Wide Web10.1007/s11280-024-01306-y27:6Online publication date: 2-Oct-2024
https://dl.acm.org/doi/10.1007/s11280-024-01306-y
Jiang SHe WCui LXu YLiu L(2023)Modeling Long- and Short-Term User Preferences via Self-Supervised Learning for Next POI RecommendationACM Transactions on Knowledge Discovery from Data10.1145/359721117:9(1-20)Online publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1145/3597211
Wang JShi YYu HWang XYan ZKong FChen HDuh WHuang HKato MMothe JPoblete B(2023)Mixed-Curvature Manifolds Interaction Learning for Knowledge Graph-aware RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591730(372-382)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591730

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