Please wait a minute...
Advanced Search
数据分析与知识发现  2022, Vol. 6 Issue (5): 77-88     https://doi.org/10.11925/infotech.2096-3467.2021.1047
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
融合谱聚类和多因素影响的兴趣点推荐方法*
郭蕾,刘文菊,王赜(),任悦强
天津工业大学计算机科学与技术学院 天津 300387
Point-of-Interest Recommendation with Spectral Clustering and Multi-Factors
Guo Lei,Liu Wenju,Wang Ze(),Ren Yueqiang
College of Computer Science and Technology, Tiangong University, Tianjin 300387, China
全文: PDF (1184 KB)   HTML ( 35
输出: BibTeX | EndNote (RIS)      
摘要 

【目的】 提高基于位置的社交网络的推荐算法的运行效率并降低稀疏数据对推荐效果的影响,提高兴趣点推荐准确率指标等。【方法】 使用自适应谱聚类方法对用户进行分组,将组内用户访问过的兴趣点组成待推荐集合,综合考虑4个方面的影响,计算待推荐集合中兴趣点的吸引力评分,向用户推荐评分较高的兴趣点。【结果】 在两种真实的基于位置的社交网络数据集Gowalla、Foursquare中进行实验。实验结果表明,推荐兴趣点个数为2时,推荐准确率分别为11.4%、7.4%,与对比方法Lore相比准确率分别提高3.2%、1.1%;运行时间为50 644.5 s、406 224.7 s,分别缩短16 961.5 s、227 248.6 s。【局限】 聚类效果的好坏对兴趣点的筛选结果有较大影响,因此所提算法对用户聚类分组效果有一定依赖性。【结论】 该算法易于执行,执行效率较高,并且可以融合各种方法充分利用LBSN这种异质网络中的丰富语义信息来提升准确率。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
郭蕾
刘文菊
王赜
任悦强
关键词 谱聚类兴趣点推荐基于位置的社交网络    
Abstract

[Objective] This paper tries to improve the recommendation algorithm for Location-Based Social Networks (LBSN) and reduce the impacts of sparse data on recommendation precision. [Methods] First, we used the adaptive spectral clustering technique to group the users. Then, we created the recommending candidates for the point of interests (POIs) visited by the users. Finally, we calculated the attracting scores of the candidate sets and generated the recommended POIs with higher scores. [Results] We examined the new model with two real LBSN data sets: Gowalla and Foursquare, and set the recommended number of POIs as 2. Our model’s precision reached 11.4% and 7.4%, which were 3.2% and 1.1% higher than the Lore model. The new model’s running time reduced to 50 644.53 s and 406 224.7 s (16 961.49 s and 227 248.6 s shorter than the benchmark model). [Limitations] The clustering algorithm could influence the screening of POIs. [Conclusions] The proposed model could effectively improve the recommendation precision of heterogeneous networks (i.e.,LBSN).

Key wordsSpectral Clustering    Point of Interests Recommendation    Location Based Social Network
收稿日期: 2021-09-16      出版日期: 2022-06-21
ZTFLH:  TP391  
基金资助:*天津市自然科学基金项目(19JCYBJC15800);天津市科技重大专项与工程项目(15ZXHLGX003901);国家自然科学基金项目的研究成果之一(61702366)
通讯作者: 王赜,ORCID: 0000-0001-6971-2004     E-mail: wangze@tiangong.edu.cn
引用本文:   
郭蕾, 刘文菊, 王赜, 任悦强. 融合谱聚类和多因素影响的兴趣点推荐方法*[J]. 数据分析与知识发现, 2022, 6(5): 77-88.
Guo Lei, Liu Wenju, Wang Ze, Ren Yueqiang. Point-of-Interest Recommendation with Spectral Clustering and Multi-Factors. Data Analysis and Knowledge Discovery, 2022, 6(5): 77-88.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2021.1047      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2022/V6/I5/77
Fig.1  LBSN网络结构
Fig.2  用户访问顺序模式
比较项 Foursqure Gowalla
签到记录数 4 648 106 2 688 134
用户数 18 828 7 272
兴趣点数 167 310 62 764
好友关系数 59 254 80 838
兴趣点种类数 229 177
Table 1  两种数据集的基本信息
Fig.3  Gowalla数据集兴趣点推荐准确率和召回率
Fig.4  Foursquare数据集兴趣点推荐准确率和召回率
Fig.5  兴趣点推荐F1值对比
Fig.6  三种算法耗时
Fig.7  不同因素对准确率的影响
Fig.8  不同因素对召回率的影响
[1] Li X, Jiang M M, Hong H T, et al. A Time-Aware Personalized Point-of-Interest Recommendation via High-Order Tensor Factorization[J]. ACM Transactions on Information Systems, 2017, 35(4): Article No.31.
[2] Liu Y, Yang H, Sun G X, et al. Collaborative Filtering Recommendation Algorithm Based on Multi-Relationship Social Network[J]. Ingénierie Des Systèmes d Information, 2020, 25(3): 359-364.
doi: 10.18280/isi.250310
[3] Cai W J, Wang Y F, Lv R H, et al. An Efficient Location Recommendation Scheme Based on Clustering and Data Fusion[J]. Computers & Electrical Engineering, 2019, 77: 289-299.
[4] Hu S H, Tu Z Y, Wang Z J, et al. A POI-Sensitive Knowledge Graph Based Service Recommendation Method[C]// Proceedings of the 2019 IEEE International Conference on Services Computing. IEEE, 2019: 197-201.
[5] Cheng C, Yang H, King I, et al. Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks[C]// Proceedings of the 2012 AAAI Conference on A.pngicial Intelligence. 2012, 26(1): 17-23.
[6] Zhang J D, Chow C Y. iGSLR: Personalized Geo-Social Location Recommendation: A Kernel Density Estimation Approach[C]// Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2013: 334-343.
[7] Pan Z G, Cui L, Wu X Y, et al. Deep Potential Geo-Social Relationship Mining for Point-of-Interest Recommendation[J]. IEEE Access, 2019, 7: 99496-99507.
doi: 10.1109/ACCESS.2019.2930311
[8] 许朝, 孟凡荣, 袁冠, 等. 融合地点影响力的兴趣点推荐算法[J]. 计算机应用, 2019, 39(11): 3178-3183.
[8] ( Xu Chao, Meng Fanrong, Yuan Guan, et al. Point-of-Interest Recommendation Algorithm Combining Location Influence[J]. Journal of Computer Applications, 2019, 39(11): 3178-3183.)
[9] Ozsoy M G, Polat F, Alhajj R. Time Preference Aware Dynamic Recommendation Enhanced with Location, Social Network and Temporal Information[C]// Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 2016: 909-916.
[10] Ding R F, Chen Z Z, Li X L. Spatial-Temporal Distance Metric Embedding for Time-Specific POI Recommendation[J]. IEEE Access, 2018, 6: 67035-67045.
doi: 10.1109/ACCESS.2018.2869994
[11] Rosário D L, Machado K, et al. TEMMUS: A Mobility Predictor Based on Temporal Markov Model with User Similarity[C]// Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. 2019: 594-607.
[12] Zhang J D, Chow C Y. GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations[C]// Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2015: 443-452.
[13] Zhang J D, Chow C Y, Li Y H. LORE: Exploiting Sequential Influence for Location Recommendations[C]// Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2014: 103-112.
[14] Li X, Wang Z J, Hu R L, et al. Recommendation Algorithm Based on Improved Spectral Clustering and Transfer Learning[J]. Pattern Analysis and Applications, 2019, 22(2): 633-647.
doi: 10.1007/s10044-017-0671-2
[15] 刘真, 王娜娜, 王晓东, 等. 位置社交网络中谱嵌入增强的兴趣点推荐算法[J]. 通信学报, 2020, 41(3): 197-206.
[15] ( Liu Zhen, Wang Nana, Wang Xiaodong, et al. Spectral Clustering and Embedding-Enhanced POI Recommendation in Location-Based Social Network[J]. Journal on Communications, 2020, 41(3): 197-206.)
[16] Luxburg U. A Tutorial on Spectral Clustering[J]. Statistics and Computing, 2007, 17(4): 395-416.
doi: 10.1007/s11222-007-9033-z
[17] 孔万增, 孙志海, 杨灿, 等. 基于本征间隙与正交特征向量的自动谱聚类[J]. 电子学报, 2010, 38(8): 1880-1885.
[17] ( Kong Wanzeng, Sun Zhihai, Yang Can, et al. Automatic Spectral Clustering Based on Eigengap and Orthogonal Eigenvector[J]. Acta Electronica Sinica, 2010, 38(8): 1880-1885.)
[18] Jaccard P. The Distribution of the Flora in the Alpine Zone[J]. New Phytologist, 1912, 11(2): 37-50.
doi: 10.1111/j.1469-8137.1912.tb05611.x
[19] Kim D, Seo D, Cho S, et al. Multi-Co-Training for Document Classification Using Various Document Representations: TF-IDF, LDA, and Doc2Vec[J]. Information Sciences, 2019, 477: 15-29.
doi: 10.1016/j.ins.2018.10.006
[20] Yuan H L, Tang Y C, Sun W J, et al. A Detection Method for Android Application Security Based on TF-IDF and Machine Learning[J]. PLoS One, 2020, 15(9): e0238694.
doi: 10.1371/journal.pone.0238694
[21] Feng W L, Zhu Q Y, Zhuang J, et al. An Expert Recommendation Algorithm Based on Pearson Correlation Coefficient and FP-Growth[J]. Cluster Computing, 2019, 22(3): 7401-7412.
doi: 10.1007/s10586-017-1576-y
[22] Shi J B, Malik J. Normalized Cuts and Image Segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.
doi: 10.1109/34.868688
[23] Hofmeyr D P. Improving Spectral Clustering Using the Asymptotic Value of the Normalized Cut[J]. Journal of Computational and Graphical Statistics, 2019, 28(4): 980-992.
doi: 10.1080/10618600.2019.1593180
[24] Silverman B W. Density Estimation for Statistics and Data Analysis[M]. New York: Routledge, 2018.
[25] Ogundele T J, Chow C Y, Zhang J D. SoCaST: Exploiting Social, Categorical and Spatio-Temporal Preferences for Personalized Event Recommendations[C]// Proceedings of the 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 3rd International Symposium of Creative Computing. IEEE, 2017: 38-45.
[1] 席运江, 杜蝶蝶, 廖晓, 仉学红. 基于超网络的企业微博用户聚类研究及特征分析*[J]. 数据分析与知识发现, 2020, 4(8): 107-118.
[2] 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.
[3] 李湘东, 高凡, 李悠海. 共通语义空间下的跨文献类型文本自动分类研究*[J]. 数据分析与知识发现, 2018, 2(9): 66-73.
[4] 陈梅梅, 薛康杰. 基于改进张量分解模型的个性化推荐算法研究*[J]. 数据分析与知识发现, 2017, 1(3): 38-45.
[5] 毕强, 刘健, 鲍玉来. 基于语义相似度的文本聚类研究*[J]. 数据分析与知识发现, 2016, 32(12): 9-16.
[6] 张志武. 跨领域迁移学习产品评论情感分析[J]. 现代图书情报技术, 2013, (6): 49-54.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn