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Applied Intelligence

, Volume 49, Issue 3, pp 858–871 | Cite as

Content-aware point-of-interest recommendation based on convolutional neural network

  • Shuning Xing
  • Fang’ai LiuEmail author
  • Qianqian Wang
  • Xiaohui Zhao
  • Tianlai Li
Article
  • 97 Downloads

Abstract

Point-of-interest (POI) recommendation has become an important approach to help people discover attractive locations. But the extreme sparsity of the user-POI matrix creates a severe challenge. To address this challenge, researchers have begun to explore the review content information for POI recommendations. Existing methods are based on bag-of-words or embedding techniques which leads to a shallow understanding of user preference. In order to capture valuable information about user preference, we propose a content-aware POI recommendation based on convolutional neural network (CPC). We utilize a convolutional neural network as the foundation of a unified POI recommendation framework and introduce the three types of content information, including POI properties, user interests and sentiment indications. The experimental results indicate that convolutional neural network is very capable of capturing semantic and sentiment information from review content and demonstrate that the relevant information in reviews can improve POI recommendation performance on location-based social networks.

Keywords

Point-of-interest Location recommendation Convolutional neural network Content-aware 

Notes

Acknowledgements

This work was supported by the following grants: National Natural Science Foundation of China (No.61772321, No.61572301, No.61602282, No.90612003), Natural Science Foundation of Shandong Province (No. ZR2013FM008, No. ZR2016FP07), the Open Research Fund from Shandong Provincial Key Laboratory of Computer Network (No.SDKLCN-2016-01), Innovation Foundation of Science and Technology Development Center of Ministry of Education and New H3C Group (2017A15047).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanChina

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