郭旦怀, 张鸣珂, 贾楠, 王彦棡. 融合深度学习技术的用户兴趣点推荐研究综述[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1890-1902. DOI: 10.13203/j.whugis20200334
引用本文: 郭旦怀, 张鸣珂, 贾楠, 王彦棡. 融合深度学习技术的用户兴趣点推荐研究综述[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1890-1902. DOI: 10.13203/j.whugis20200334
GUO Danhuai, ZHANG Mingke, JIA Nan, WANG Yangang. Survey of Point-of-Interest Recommendation Research Fused with Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1890-1902. DOI: 10.13203/j.whugis20200334
Citation: GUO Danhuai, ZHANG Mingke, JIA Nan, WANG Yangang. Survey of Point-of-Interest Recommendation Research Fused with Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1890-1902. DOI: 10.13203/j.whugis20200334

融合深度学习技术的用户兴趣点推荐研究综述

Survey of Point-of-Interest Recommendation Research Fused with Deep Learning

  • 摘要: 近年来,用户兴趣点(point of interest,POI)推荐是基于位置的社会网络(location-based social network,LBSN)研究的热门话题,POI推荐不仅可以帮助用户找到心仪的POI,也可为商家带来可观收益。深度学习技术因可以更有效地捕获用户与物品间的非线性关系,逐渐应用到推荐系统任务中。对近年来结合深度学习技术的用户POI推荐的研究进行综述。首先介绍了用户POI推荐与传统推荐任务的区别,并介绍了可以提高推荐任务模型性能的多种影响因素;随后将深度学习应用到POI推荐的方式分为4类:POI的向量化学习、深度协同过滤、从辅助内容中提取特征和利用循环神经网络进行序列推荐,并阐述了深度学习技术在这些方式中的应用效果与优势;最后对结合深度学习技术的用户POI推荐的发展方向进行了总结与展望。

     

    Abstract: Point-of-interest (POI) recommendation has emerged as a focal point in the research of location-based social network (LBSN) in recent years. It can help users find their favorite venue and bring considerable benefits to businesses. Nowadays, deep learning is gradually applied to the task of recommendation system because it can capture the nonlinear relationship between users and items more effectively. This paper thus focuses on recent research on POI recommendation combined with deep learning. Firstly, we introduce the difference between POI recommendation and other traditional recommendation tasks and illustrate various influencing factors that can improve the performance of the model. Then, the methods of applying deep learning to POI recommendation are divided into four categories, including POI embedding, deep collaborative filtering, feature extraction from side information, and sequence recommendation using recurrent neural network (RNN). We also investigate the development of user models performance and advantages combined with deep learning in these different aspects of applications. Finally, we summarize and look forward to the development of POI recommendation research combined with deep learning.

     

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