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Personalized Context-aware Collaborative Online Activity Prediction

Authors:

Depeng JinAuthors Info & Claims

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

Article No.: 132, Pages 1 - 28

https://doi.org/10.1145/3369829

Published: 14 September 2020 Publication History

Abstract

With the rapid development of Internet services and mobile devices, nowadays, users can connect to online services anytime and anywhere. Naturally, user's online activity behavior is coupled with time and location contexts and highly influenced by them. Therefore, personalized context-aware online activity modelling and prediction is very meaningful and necessary but also very challenging, due to the complicated relationship between users, activities, spatial and temporal contexts and data sparsity issues. To tackle the challenges, we introduce offline check-in data as auxiliary data and build a user-location-time-activity 4D-tensor and a location-time-POI 3D-tensor, aiming to model the relationship between different entities and transfer semantic features of time and location contexts among them. Accordingly, in this paper we propose a transfer learning based collaborative tensor factorization method to achieve personalized context-aware online activity prediction. Based on real-world datasets, we compare the performance of our method with several state-of-the-arts and demonstrate that our method can provide more effective prediction results in the high sparsity scenario. With only 30% of observed time and location contexts, our solution can achieve 40% improvement in predicting user's Top5 activity behavior in new time and location scenarios. Our study is the first step forward for transferring knowledge learned from offline check-in behavior to online activity prediction to provide better personalized context-aware recommendation services for mobile users.

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Cited By

Mo ZXiang HDi X(2024)Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility PredictionACM Transactions on Spatial Algorithms and Systems10.1145/367322710:4(1-25)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3673227
Belal YBen Mokhtar SHaddadi HWang JMashhadi A(2024)Survey of Federated Learning Models for Spatial-Temporal Mobility ApplicationsACM Transactions on Spatial Algorithms and Systems10.1145/366608910:3(1-39)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3666089
Khaokaew YXue HSalim F(2024)MAPLEProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435148:1(1-25)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643514
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Index Terms

Personalized Context-aware Collaborative Online Activity Prediction
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering
    2. Spatial-temporal systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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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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Published: 14 September 2020

Published in IMWUT Volume 3, Issue 4

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

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https://dl.acm.org/doi/10.1145/3673227
Belal YBen Mokhtar SHaddadi HWang JMashhadi A(2024)Survey of Federated Learning Models for Spatial-Temporal Mobility ApplicationsACM Transactions on Spatial Algorithms and Systems10.1145/366608910:3(1-39)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3666089
Khaokaew YXue HSalim F(2024)MAPLEProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435148:1(1-25)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643514
Zhang YLi TYuan YXu FYang FSun FLi Y(2024)Demand-driven Urban Facility Visit PredictionACM Transactions on Intelligent Systems and Technology10.1145/362523315:2(1-24)Online publication date: 22-Feb-2024
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Zheng LCao YJiang RTaura KShen YLi SYoshikawa M(2024)Enhancing Privacy of Spatiotemporal Federated Learning Against Gradient Inversion AttacksDatabase Systems for Advanced Applications10.1007/978-981-97-5552-3_31(457-473)Online publication date: 2-Jul-2024
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Zhang XWang QYe ZYing HYu D(2023)Federated Representation Learning With Data Heterogeneity for Human Mobility PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.325202924:6(6111-6122)Online publication date: 16-Mar-2023
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