Spatial-Aware Hierarchical Collaborative Deep Learning for POI recommendation

Hongzhi Yin, Weiqing Wang, Hao Wang, Ling Chen, Xiaofang Zhou

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151 Citations (Scopus)


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.

Original languageEnglish
Article number8013107
Pages (from-to)2537-2551
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number11
Publication statusPublished - Nov 2017
Externally publishedYes


  • cold start
  • deep learning
  • multimodal modeling
  • POI recommendation
  • spatial-aware user modeling

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