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数据分析与知识发现  2019, Vol. 3 Issue (8): 30-39    DOI: 10.11925/infotech.2096-3467.2018.0764
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于地理信息偏好修正和社交关系偏好隐式分析的POI推荐 *
温彦1(),马立健1,曾庆田2,郭文艳1
1山东科技大学计算机科学与技术学院 青岛 266590
2山东科技大学电子通信与物理学院 青岛 266590
POI Recommendation Based on Geographic and Social Relationship Preferences
Yan Wen1(),Lijian Ma1,Qingtian Zeng2,Wenyan Guo1
1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2College of Electronic Communications and Physics, Shandong University of Science and Technology, Qingdao 266590, China
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摘要 

【目的】利用用户签到记录的地理位置信息和用户社交关系开展对兴趣点(POI)推荐问题的研究。【方法】基于签到地理位置所隐含的用户偏好及用户社交关系的偏好特征两方面提高兴趣点推荐质量, 提出一种推荐模型MFDR, 对已有工作进行如下改进: 采用距离熵描述不同签到地理位置所反映的用户偏好并用于修正用户兴趣矩阵; 引入用户关系兴趣矩阵用于细化社交关系的兴趣偏好, 基于正则矩阵分解法求解用户兴趣矩阵和用户关系兴趣矩阵, 并采用联合分解方式保障结果的一致性。【结果】在Gowalla和Brightkite签到数据集上进行实验, 结果优于已有的POI推荐工作。当隐语义数为10、推荐数为10时, 该模型在Gowalla上推荐的准确率为4.47%, 召回率为9.95%, 分别比其他兴趣点推荐模型高至少30.71%和28.93%。【局限】受朋友关系及其共同签到数据的稀疏性影响, 实验样本数量有待扩充, 所得结论有待进一步推广。【结论】基于地理信息偏好修正和社交关系隐式分析的POI推荐方法具有较好的推荐效果。

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温彦
马立健
曾庆田
郭文艳
关键词 推荐系统基于位置的社交网络矩阵分解兴趣点    
Abstract

[Objective] This study tries to improve the POI recommendation based on user’s geographic information and social relationships. [Methods] First, we proposed a MFDR model (MF with Distance-entropy and Refined-social-regularization), which introduced the concept of distance-entropy to refine user’s preferences and the frequency-based user-interest-matrix. Then, we applied the user-relationship-interest-matrix to refine the preferences with their social-relationship. Finally, we used the regularization-based matrix factorization method to factorize the user-preference-matrix and user-relationship-interest-matrix to ensure their consistency. [Results] We examined the new model with Gowalla and Brightkite check-in datasets, and found it outperformed existing POI recommendation algorithms. When the number of latent factors was 10 and the number of recommended POI was 10, the precision and recall of MFDR on Gowalla reached 4.47% and 9.95%. These results were 30.71% and 28.93% higher than those of traditional POI recommendation models. [Limitations] The expeimental datasets need to be expanded. [Conclusions] The proposed MFDR model based on geographical preference refinement and social-relationship preference implicit analysis is an effective way to recommend POI.

Key wordsRecommendation System    Location Based Social Networks    Matrix Factorization    Point of Interest    Entropy
收稿日期: 2018-07-15     
中图分类号:  TP181 G35  
基金资助:*本文系教育部人文社会科学研究青年基金项目“基于网络大数据的突发灾害社会影响动态跟踪与评估方法”(17YJCZH187);国家自然科学基金项目“基于表示模型的在线社交网络信息传播模型研究”(61702306);青岛市哲学社会科学规划项目“基于大数据分析的突发灾害社会影响评估方法”的研究成果之一(QDSKL1801131)
通讯作者: 温彦     E-mail: wenyanxxxy@163.com
引用本文:   
温彦,马立健,曾庆田,郭文艳. 基于地理信息偏好修正和社交关系偏好隐式分析的POI推荐 *[J]. 数据分析与知识发现, 2019, 3(8): 30-39.
Yan Wen,Lijian Ma,Qingtian Zeng,Wenyan Guo. POI Recommendation Based on Geographic and Social Relationship Preferences. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0764.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0764
图1  MFDR推荐算法流程
图2  实验结果对比
图3  不同隐语义维度D的实验结果对比
图4  不同训练集划分下实验结果对比
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