research-article

DAN-SNR: A Deep Attentive Network for Social-aware Next Point-of-interest Recommendation

Authors:

Keqing HeAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 21, Issue 1

Article No.: 2, Pages 1 - 27

https://doi.org/10.1145/3430504

Published: 22 December 2020 Publication History

Abstract

Next (or successive) point-of-interest (POI) recommendation, which aims to predict where users are likely to go next, has recently emerged as a new research focus of POI recommendation. Most of the previous studies on next POI recommendation attempted to incorporate the spatiotemporal information and sequential patterns of user check-ins into recommendation models to predict the target user's next move. However, few of the next POI recommendation approaches utilized the social influence of each user's friends. In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR. In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner. Moreover, we design and implement two parallel channels to capture short-term user preference and long-term user preference as well as social influence, respectively. By leveraging multi-head self-attention, the DAN-SNR can model long-range dependencies between any two historical check-ins efficiently and weigh their contributions to the next destination adaptively. We also carried out a comprehensive evaluation using large-scale real-world datasets collected from two popular location-based social networks, namely, Gowalla and Brightkite. Experimental results indicate that the DAN-SNR outperforms seven competitive baseline approaches regarding recommendation performance and is highly efficient among six neural-network-based methods, four of which utilize the attention mechanism.

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Cited By

Chen JYe WKang S(2024)Learning user preferences from Multi-Contextual Sequence influences for next POI recommendationElectronic Research Archive10.3934/era.202402432:1(486-504)Online publication date: 2024
https://doi.org/10.3934/era.2024024
Gao QHong JXu XKuang PZhou FTrajcevski G(2024)Predicting Human Mobility Via Self-Supervised Disentanglement LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3317175(1-16)Online publication date: 2024
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Acharya MMohbey KRajput D(2024)Long-Term Preference Mining With Temporal and Spatial Fusion for Point-of-Interest RecommendationIEEE Access10.1109/ACCESS.2024.335493412(11584-11596)Online publication date: 2024
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Index Terms

DAN-SNR: A Deep Attentive Network for Social-aware Next Point-of-interest Recommendation
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Social recommendation

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 21, Issue 1

Visions Paper, Regular Papers, SI: Blockchain in E-Commerce, and SI: Human-Centered Security, Privacy, and Trust in the Internet of Things

February 2021

534 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3441681

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Copyright © 2020 ACM.

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Publication History

Published: 22 December 2020

Accepted: 01 October 2020

Revised: 01 September 2020

Received: 01 April 2020

Published in TOIT Volume 21, Issue 1

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Cited By

Chen JYe WKang S(2024)Learning user preferences from Multi-Contextual Sequence influences for next POI recommendationElectronic Research Archive10.3934/era.202402432:1(486-504)Online publication date: 2024
https://doi.org/10.3934/era.2024024
Gao QHong JXu XKuang PZhou FTrajcevski G(2024)Predicting Human Mobility Via Self-Supervised Disentanglement LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3317175(1-16)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3317175
Acharya MMohbey KRajput D(2024)Long-Term Preference Mining With Temporal and Spatial Fusion for Point-of-Interest RecommendationIEEE Access10.1109/ACCESS.2024.335493412(11584-11596)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3354934
Yang YZhou SWeng HWang DZhang XYu DDeng S(2024)Siamese learning based on graph differential equation for Next-POI recommendationApplied Soft Computing10.1016/j.asoc.2023.111086150(111086)Online publication date: Jan-2024
https://doi.org/10.1016/j.asoc.2023.111086
Ma YWang ZHuang LWang J(2023)CLHHN: Category-aware Lossless Heterogeneous Hypergraph Neural Network for Session-based RecommendationACM Transactions on the Web10.1145/362656918:1(1-37)Online publication date: 14-Nov-2023
https://dl.acm.org/doi/10.1145/3626569
Sun KQian TLi CMa XLi QZhong MZhu YLiu M(2023)Pre-Training Across Different Cities for Next POI RecommendationACM Transactions on the Web10.1145/360555417:4(1-27)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1145/3605554
Wang YLee A(2023)Movie Account Recommendation on InstagramACM Transactions on Internet Technology10.1145/357985223:1(1-21)Online publication date: 13-Jan-2023
https://dl.acm.org/doi/10.1145/3579852
Huang LMa YLiu YDanny Du BWang SLi D(2023)Position-Enhanced and Time-aware Graph Convolutional Network for Sequential RecommendationsACM Transactions on Information Systems10.1145/351170041:1(1-32)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3511700
Li HYue PLi SZhang CYang C(2023)Spatio-temporal intention learning for recommendation of next point-of-interestGeo-spatial Information Science10.1080/10095020.2023.217942827:2(384-397)Online publication date: 5-Apr-2023
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Cui PYin BXu B(2023)The application of social recommendation algorithm integrating attention model in movie recommendationScientific Reports10.1038/s41598-023-43511-113:1Online publication date: 7-Oct-2023
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