POI Representation Learning by a Hybrid Model
Yurui Li, Hongmei Chen, Lizhen Wang, Qing Xiao · 2019
Point of Interest (POI) is the core element of check-in data. It is an effective way to represent POI by distributed representation which can encode the information of POI into a continuous vector space. In this work, we present a hybrid model that map the concepts of network representation learning (NRL) to learn the position of POI and map the concepts of nature language processing (NLP) to learn the category of POI. The results, which are in vector form, can be widely applied to various location based services (LBS) without complicated artificial feature extraction. Further, it can also improve the performance of LBS. By a range of experiments on real datasets, we demonstrate our model's capability at characterizing POI. We also verify the effectiveness of the hybrid model on POI recommendation.