融合潜在狄利克雷分布与元路径分析的用户相关性度量方法

doi:10.11772/j.issn.1001-9081.2019040728

计算机应用 ›› 2019, Vol. 39 ›› Issue (11): 3288-3292.DOI: 10.11772/j.issn.1001-9081.2019040728

融合潜在狄利克雷分布与元路径分析的用户相关性度量方法

徐红艳, 王丹, 王富海, 王嵘冰

辽宁大学信息学院, 沈阳 110036

收稿日期:2019-04-28 修回日期:2019-07-19 出版日期:2019-11-10 发布日期:2019-08-26
通讯作者: 王嵘冰
作者简介:徐红艳(1972-),女,辽宁丹东人,副教授,硕士,主要研究方向:deep Web、个性化推荐、数据挖掘;王丹(1994-),女,辽宁丹东人,硕士研究生,CCF会员,主要研究方向:个性化推荐、数据挖掘;王富海(1990-),男,辽宁海城人,硕士研究生,CCF会员,主要研究方向:深度学习、个性化推荐;王嵘冰(1979-),男,辽宁沈阳人,副教授,博士,CCF会员,主要研究方向:大数据分析、云计算。
基金资助:
国家自然科学基金资助项目（71771110）；辽宁省社会科学规划基金资助项目（L18AGL007）；吉林大学符号计算与知识工程教育部重点实验室项目（93K172018K01）。

User relevance measure method combining latent Dirichlet allocation and meta-path analysis

XU Hongyan, WANG Dan, WANG Fuhai, WANG Rongbing

School of Information, Liaoning University, Shenyang Liaoning 110036, China

Received:2019-04-28 Revised:2019-07-19 Online:2019-08-26 Published:2019-11-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (71771110), the Social Science Planning Foundation of Liaoning Province of China (L18AGL007), the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education in Jilin University (93K172018K01).

摘要/Abstract

摘要： 用户相关性度量是异构信息网络研究的基础与核心。现有的用户相关性度量方法由于未充分开展多维度分析和链路分析，其准确性尚存在提升空间。为此，提出了一种融合狄利克雷分布（LDA）与元路径分析的用户相关性度量方法。首先利用LDA进行主题建模，通过分析网络中节点的内容来计算节点的相关性；然后，引入元路径来刻画节点间关系类型，通过关联度量（DPRel）方法对异构信息网络中的用户进行相关性测量；接着，将节点的相关性融入到用户相关性度量计算中；最后，采用IMDB真实电影数据集进行实验，将所提方法和嵌入LDA主题模型的协同过滤推荐方法（ULR-CF）、基于元路径的相关性度量方法（PathSim）进行了对比分析。实验结果表明，所提方法能够克服数据稀疏性弊端，提高用户相关性度量的准确性。

关键词: 用户相关性, 异构信息网络, 主题模型, 元路径, 度量

Abstract: User relevance measure is the foundation and core of heterogeneous information network research. The existing user relevance measure methods still have improvement space due to insufficient multi-dimensional analysis and link analysis. Aiming at the fact, a user relevance measure method combining Latent Dirichlet Allocation (LDA) and meta-path analysis was proposed. Firstly, the LDA was used to model the topic, and the relevance of nodes was analyzed by the node contents in the network. Secondly, the meta-path was introduced to describe the relationship type between nodes, and relevance measure was carried out for users in heterogeneous information network by relevance measure method (DPRel). Thirdly, the relevance of nodes was incorporated into the calculation of user relevance measure. Finally, the experiment was carried out on IMDB real movie dataset, and the proposed method was compared with the collaborative filtering recommendation method embedded in LDA topic model ULR-CF (Unifying LDA and Ratings Collaborative Filtering) and meta-path based similarity method (PathSim).The experimental results show that the proposed method can overcome the drawback of data sparsity and improve the accuracy of user relevance measure.

Key words: user relevance, heterogeneous information network, topic model, meta-path, measure

中图分类号:

TP302.1

徐红艳, 王丹, 王富海, 王嵘冰. 融合潜在狄利克雷分布与元路径分析的用户相关性度量方法[J]. 计算机应用, 2019, 39(11): 3288-3292.

XU Hongyan, WANG Dan, WANG Fuhai, WANG Rongbing. User relevance measure method combining latent Dirichlet allocation and meta-path analysis[J]. Journal of Computer Applications, 2019, 39(11): 3288-3292.

参考文献

[1] 党博,姜久雷. 基于评分差异度和用户偏好的协同过滤算法[J]. 计算机应用, 2016, 36(4):1050-1053.(DANG B, JIANG J L. Collaborative filtering recommendation algorithm based on score difference level and user preference[J]. Journal of Computer Applications, 2016, 36(4):1050-1053.)
[2] GENG X, LIU T, QIN T, et al. Feature selection for ranking[C]//Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM, 2007:407-414.
[3] DAHM N, BUNKE H, CAELLI T, et al. Efficient subgraph matching using topological node feature constraints[J]. Pattern Recognition, 2015, 48(2):317-330.
[4] 杨家红,胡涛,任沙,等. 引入一种基于元路径的链路预测机制的好友推荐算法[J]. 小型微型计算机系统, 2017, 38(4):726-731. (YANG J H, HU T, REN S, et al. Friend recommendation algorithm introducing link prediction mechanism based on meta-path[J]. Journal of Chinese Computer Systems, 2017, 38(4):726-731.)
[5] 于亚新,张海军. EBSN中基于潜在好友关系的活动推荐算法[J].计算机科学, 2018, 45(3):198-205. (YU Y X, ZHANG H J. Activity recommendation algorithm based on latent friendships in EBSN[J]. Computer Science, 2018, 45(3):198-205.)
[6] BLEI D M, NG A Y, JORDAN M I. Latent Dirich allocation[J]. Journal of Machine Learning Research, 2003, 3:993-1022.
[7] GUPTA M, KUMAR P, BHASKER B. DPRel:a meta-path based relevance measure for mining heterogeneous networks[J]. Information Systems Frontiers, 2019, 21(5):979-995.
[8] 冯勇,张丽颖,顾兆旭,等. 面向高校多源异构数据环境的元数据集成方法[J]. 辽宁大学学报(自然科学版), 2019, 46(2):135-141.(FENG Y, ZHANG L Y, GU Z X, et al. A metadata integration method oriented multi-source heterogeneous data environment of colleges[J]. Journal of Liaoning University (Natural Science Edition), 2019, 46(2):135-141.)
[9] SALTON G, WONG A, YANG C S. A vector space model for automatic indexing[J]. Communications of the ACM, 1975, 18(11):613-620.
[10] DEERWESTER S, DUMAIS S T, FURNAS G W, et al. Indexing by latent semantic[J]. Journal of the American Society for Information Science, 1990, 41(6):391-407.
[11] JEH G, WIDOM J. Scaling personalized Web search[C]//Proceedings of the 12th International Conference on World Wide Web. New York:ACM, 2003:271-279.
[12] KUSUMOTO M, MAEHARA T, KAWARABAYASHI K I. Scalable similarity search for SimRank[C]//Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. New York:ACM, 2014:325-336.
[13] HUANG Z, ZHENG Y, CHENG R, et al. Meta structure:computing relevance in large heterogeneous information networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2016:1595-1604.
[14] MENG C, CHENG R, MANIU S, et al. Discovering meta-paths in large heterogeneous information networks[C]//Proceedings of the 24th International Conference on World Wide Web. Republic and Canton of Geneva, Switzerland:International World Wide Web Conferences Steering Committee, 2015:754-764.
[15] SUN Y, HAN J, YAN X, et a. PathSim:meta path-based top-K similarity search in heterogeneous information networks[J]. Proceedings of the VLDB Endowment, 2011, 4(11):992-1003.
[16] 丁少衡,姬东鸿,王路路. 基于用户属性和评分的协同过滤推荐算法[J]. 计算机工程与设计, 2015, 36(2):487-491. (DING S H, JI D H, WANG L L, et al. Collaborative filtering recommendation algorithm based on user attributes and scores[J]. Computer Engineering and Design, 2015, 36(2):487-491.)
[17] 邱庆羽,李婧,全兵,等. 基于文献信息网络语义特征的相似性搜索[J]. 计算机应用, 2018, 38(5):1327-1333. (QIU Q Y, LI J, QUAN B, et al. Similarity search based on semantic features of bibliographic information network[J]. Journal of Computer Applications, 2018, 38(5):1327-1333.)
[18] 高娜,杨明. 嵌入LDA主题模型的协同过滤推荐算法[J]. 计算机科学, 2016, 43(3):57-61. (GAO N, YANG M. Topic model embedded in collaborative filtering recommendation algorithm[J]. Computer Science, 2016, 43(3):57-61.)

融合潜在狄利克雷分布与元路径分析的用户相关性度量方法

User relevance measure method combining latent Dirichlet allocation and meta-path analysis

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