research-article

Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation

Authors:

Xiaofang ZhouAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 29, Issue 11

Pages 2537 - 2551

https://doi.org/10.1109/TKDE.2017.2741484

Published: 01 November 2017 Publication History

Abstract

Point-of-interest (POI) recommendation has become an important way to help people discover attractive and interesting places, especially when they travel out of town. However, the extreme sparsity of user-POI matrix and cold-start issues severely hinder the performance of collaborative filtering-based methods. Moreover, user preferences may vary dramatically with respect to the geographical regions due to different urban compositions and cultures. To address these challenges, we stand on recent advances in deep learning and propose a Spatial-Aware Hierarchical Collaborative Deep Learning model (SH-CDL). The model jointly performs deep representation learning for POIs from heterogeneous features and hierarchically additive representation learning for spatial-aware personal preferences. To combat data sparsity in spatial-aware user preference modeling, both the collective preferences of the public in a given target region and the personal preferences of the user in adjacent regions are exploited in the form of social regularization and spatial smoothing. To deal with the multimodal heterogeneous features of the POIs, we introduce a late feature fusion strategy into our SH-CDL model. The extensive experimental analysis shows that our proposed model outperforms the state-of-the-art recommendation models, especially in out-of-town and cold-start recommendation scenarios.

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Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 29, Issue 11

Nov. 2017

255 pages

ISSN:1041-4347

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1041-4347 © 2016 IEEE.

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IEEE Educational Activities Department

United States

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Published: 01 November 2017

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Selamat AIbrahim RAlrashidi MFujita H(2024)Social Recommender System Based on CNN Incorporating Tagging and Contextual FeaturesJournal of Cases on Information Technology10.4018/JCIT.33552426:1(1-20)Online publication date: 7-Jan-2024
https://dl.acm.org/doi/10.4018/JCIT.335524
Zheng RQu LChen TCui LShi YYin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645696(3930-3939)Online publication date: 13-May-2024
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Wu XZhou HShi YYao WHuang XLiu NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645494(3566-3575)Online publication date: 13-May-2024
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Jiang WGao XXu GChen TYin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Challenging Low Homophily in Social RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645460(3476-3484)Online publication date: 13-May-2024
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