顾及微地图推荐因子的深度信念网络

牛雪磊, 杨军, 闫浩文

牛雪磊, 杨军, 闫浩文. 顾及微地图推荐因子的深度信念网络[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230053
引用本文: 牛雪磊, 杨军, 闫浩文. 顾及微地图推荐因子的深度信念网络[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230053
NIU Xuelei, YANG Jun, YAN Haowen. Deep Belief Networks Considering WeMaps' Recommendation Factors[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230053
Citation: NIU Xuelei, YANG Jun, YAN Haowen. Deep Belief Networks Considering WeMaps' Recommendation Factors[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230053

顾及微地图推荐因子的深度信念网络

基金项目: 

国家自然科学基金(42261067);2021年度中央引导地方科技发展资金(2021-51);兰州市人才创新创业项目(2020-RC-22);兰州交通大学天佑创新团队(TY202002)。

详细信息
    作者简介:

    牛雪磊,博士生,主要从事深度学习、地理信息系统等研究。E-mail:NXLnxl186186@163.com

    通讯作者:

    杨军,博士,教授,主要从事深度学习、三维模型空间分析、遥感大数据智能解译、地理信息系统等研究。E-mail:yangj@mail.lzjtu.cn

Deep Belief Networks Considering WeMaps' Recommendation Factors

  • 摘要: 在微地图推荐系统的冷启动阶段,针对传统深度信念网络无法高效挖掘数据中的细粒度信息而导致推荐精度下降的问题,提出了顾及微地图推荐因子的深度信念网络。利用聚类的结构化信息和深度信念网络中丰富的语义信息缓解推荐系统在冷启动阶段推荐精度低的问题,并且挖掘出用户和微地图的隐语义交互信息,达到“千人千面”的推荐效果。首先,采用针对噪声数据的基于密度的空间聚类算法对用户与微地图历史样本进行聚类;然后,选取出核心点(即推荐因子)、边界点及离群点,将聚类后的推荐因子构建成不同的可视层与隐含层;最后,将受限玻尔兹曼机的可视神经元替换为高斯单元,利用深度信念网络对输入数据从低层到高层进行逐层微调训练,以提升推荐的准确性。在美食、自驾、旅游、校园四类微地图数据集上推荐的精度分别为0.77532、0.76818、0.77518、0.77464,推荐的均方误差分别为0.19078、0.19476、0.19033、0.19092。实验结果表明,本文算法可以为用户准确地推荐感兴趣的微地图信息。
    Abstract: Objectives: In the cold start phase of the Wemaps' recommendation system, traditional deep belief networks have low efficiency in mining fine-grained information, resulting in a decrease in recommendation accuracy. Therefore, this paper proposes a D eep B elief network considering WeMaps' r ecommendation Fact ors (DBRFact). The structured information of clustering and rich semantic information in the deep belief networks are used to alleviate the problem of low recommendation accuracy during the cold start. In addition, the implicit semantic interaction information between the users and the WeMaps is mined to implement customized recommendations. Methods: First, a Density-Based Spatial Clustering of Application with Noise clustering algorithm is employed to cluster the historical samples of the users and the WeMaps. Secondly, the core points (i.e., recommendation factors), boundary points, and outliers are selected, and the recommendation factors are constructed into different visual and hidden layers. Finally, the visual neurons of the restricted Boltzmann machine are replaced with Gaussian units. Then, a deep belief network is used to fine-tune the input data from low level to high level to improve the recommendation accuracy. Results: The accuracies on the FoodMaps, DrivingMaps, TourMaps, and SchoolMaps datasets were 0.77532, 0.76818, 0.77518 and 0.77464, respectively, and the Root Mean Square Errors (RMSE) were 0.19078, 0.19476, 0.19033 and 0.19092, respectively. Conclusions: The experimental results have showed that the proposed algorithm can accurately recommend the interested WeMaps' information for users.
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出版历程
  • 收稿日期:  2023-06-03
  • 网络出版日期:  2023-07-16

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