POI recommendation has attracted lots of research attentions recently. There are several key factors that need to be modeled towards effective POI recommendation - POI properties, user preference and sequential momentum of check- ins. The challenge lies in how to synergistically learn multi-source heterogeneous data. Previous work tries to model multi-source information in a flat manner, using either embedding based methods or sequential prediction models in a cross-related space, which cannot generate mutually reinforce results. In this paper, a deep and broad learning approach based on a Deep Context- aware POI Recommendation (DCPR) model was proposed to structurally learn POI and user characteristics. The proposed DCPR model includes three collaborative layers, a CNN layer for POI feature mining, an RNN layer for sequential dependency and user preference modeling, and an interactive layer based on matrix factorization to jointly optimize the overall model. Experiments over three data sets demonstrate that DCPR model achieves significant improvement over state-of-the-art POI recommendation algorithms and other deep recommendation models.
|Original language||English (US)|
|Title of host publication||Proceedings - 2017 IEEE 3rd International Conference on Collaboration and Internet Computing, CIC 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - Dec 9 2017|
|Event||3rd IEEE International Conference on Collaboration and Internet Computing, CIC 2017 - San Jose, United States|
Duration: Oct 15 2017 → Oct 17 2017
|Name||Proceedings - 2017 IEEE 3rd International Conference on Collaboration and Internet Computing, CIC 2017|
|Other||3rd IEEE International Conference on Collaboration and Internet Computing, CIC 2017|
|Period||10/15/17 → 10/17/17|
Bibliographical noteFunding Information:
This work is supported in part by NSF through grants IIS-1526499, and CNS-1626432, and NSFC 61672313.
- POI Recommendation
- Spatial Temporal Modeling