Real-time event embedding for POI recommendation

Pei Yi Hao, Weng Hang Cheang, Jung Hsien Chiang

研究成果: Article

1 引文 斯高帕斯(Scopus)

摘要

Location-based social networks (LBSNs) allow users to check-in and share daily lives with others. We have witnessed very rapid development of LBSNs in recent years. Point-of-Interest (POI) recommendation is one of the core services in LBSNs. In this study, we propose a real-time POI embedding model. Instead of capturing intrinsic information, the proposed approach is able to mine real-time information of places and learn the latent representations according to the corresponding geo-tagged posts. On one hand, we employ a Convolutional Neural Networks (CNN) to mine textual information of POIs and learn their intrinsic representation. On the other hand, a multimodal embedding model of location, time and text is applied to keep monitoring posts on POIs and extracts a set of features for representing events or burst information that may attract users. Furthermore, we combine real-time POI embedding with matrix factorization method and propose a more comprehensive POI recommendation algorithm. To verify the effectiveness of our proposed method, we conduct experiments on Twitter dataset with geo-tagged tweets in NYC. Experimental results show that POI recommendation system with taking real-time event into consideration can strongly improve the performance than the one without.

原文English
頁(從 - 到)1-11
頁數11
期刊Neurocomputing
349
DOIs
出版狀態Published - 2019 七月 15

    指紋

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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