基于LDA主题模型的移动应用相似度构建方法

doi:10.11772/j.issn.1001-9081.2017.04.1075

计算机应用 ›› 2017, Vol. 37 ›› Issue (4): 1075-1082.DOI: 10.11772/j.issn.1001-9081.2017.04.1075

基于LDA主题模型的移动应用相似度构建方法

褚征¹, 于炯^1,2, 王佳玉¹, 王跃飞^1,2

1. 新疆大学软件学院, 乌鲁木齐 830008;
2. 新疆大学信息科学与工程学院, 乌鲁木齐 830046

收稿日期:2016-09-26 修回日期:2016-12-25 出版日期:2017-04-10 发布日期:2017-04-19
通讯作者: 于炯
作者简介:褚征(1991-),男,河南南阳人,硕士研究生,CCF会员,主要研究方向:数据挖掘、内存计算、绿色计算;于炯(1964-),男,新疆乌鲁木齐人,教授,博士,CCF会员,主要研究方向:网络安全、网格计算、分布式计算;王佳玉(1991-),女,黑龙江哈尔滨人,硕士研究生,CCF会员,主要研究方向:数据挖掘、移动计算;王跃飞(1991-),男,新疆乌鲁木齐人,博士研究生,主要研究方向:数据挖掘、内存计算、绿色计算。
基金资助:
国家自然科学基金资助项目（61462079，61262088，61562086，61363083）。

Construction method of mobile application similarity matrix based on latent Dirichlet allocation topic model

CHU Zheng¹, YU Jiong^1,2, WANG Jiayu¹, WANG Yuefei^1,2

1. School of Software, Xinjiang University, Urumqi Xinjiang 830008, China;
2. School of Information Science and Engineering, Xinjiang University, Urumqi Xinjiang 830046, China

Received:2016-09-26 Revised:2016-12-25 Online:2017-04-10 Published:2017-04-19
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61462079,61262088,61562086,61363083).

摘要/Abstract

摘要： 随着移动互联网的快速发展，如何从大量的移动应用中抽取有效的描述信息继而为移动用户提供有效准确的推荐策略变得尤为迫切。目前，移动应用市场对应用的推荐策略相对传统，大多是根据应用的单一属性进行推荐，如下载量、应用名称、应用分类等。针对推荐粒度过粗和推荐不准确的问题，提出了一种基于潜在狄利克雷分布（LDA）主题模型的移动应用相似度构建方法。该方法从应用的标签入手，构造应用的主题模型分布矩阵，利用该主题分布矩阵构建移动应用的相似度矩阵，同时提出了将移动应用相似度矩阵转化为可行的存储结构的方法。实验结果表明该方法是有效的，相比现有的360应用市场推荐的应用其相似度提升130%。该方法解决了移动应用推荐过程中推荐粒度过粗的问题，可使推荐结果更加准确。

关键词: 相似度矩阵, 主题模型, 隐含信息, 应用推荐, 标签

Abstract: With the rapid development of mobile Internet, how to extract effective description information from a large number of mobile applications and then provide effective and accurate recommendation strategies for mobile users becomes urgent. At present, recommendation strategies are relatively traditional, and mostly recommend applications according to the single attribute, such as downloads, application name and application classification. In order to resolve the problem that the granularity of recommended applications is too coarse and the recommendation is not accurate, a mobile application similarity matrix construction method based on Latent Dirichlet Allocation (LDA) was proposed. Started from the application labels, a topic model distribution matrix of mobile applications was constructed, which was utilized to construct mobile application similarity matrix. Meanwhile, a method for converting the mobile application similarity matrix to the viable storage structure was also proposed. Extensive experiments demonstrate the feasibility of the proposed method, and the application similarity achieves 130 percent increasement by the proposed method compared with that by the existing 360 application market. The proposed method solves the problem that the recommended granularity is too coarse in the mobile application recommendation process, so that the recommendation result is more accurate.

中图分类号:

TP274.2

褚征, 于炯, 王佳玉, 王跃飞. 基于LDA主题模型的移动应用相似度构建方法[J]. 计算机应用, 2017, 37(4): 1075-1082.

CHU Zheng, YU Jiong, WANG Jiayu, WANG Yuefei. Construction method of mobile application similarity matrix based on latent Dirichlet allocation topic model[J]. Journal of Computer Applications, 2017, 37(4): 1075-1082.

参考文献

[1] BLEI D M. Probabilistic topic models[J]. Communications of the ACM, 2012, 55(4): 77-84.
[2] 王李冬, 魏宝刚, 袁杰. 基于概率主题模型的文档聚类[J]. 电子学报, 2012, 40(11):2346-2350.(WANG L D, WEI B G, YUAN J. Document clustering based on probabilistic topic model[J]. Acta Electronica Sinica, 2012, 40(11):2346-2350.)
[3] AL-SALEMI B, AB AZIZ M J, NOAH S A. LDA-AdaBoost.MH: accelerated AdaBoost.MH based on latent Dirichlet allocation for text categorization[J]. Journal of Information Science, 2015, 41(1): 27-40.
[4] FOULDS J, BOYLES L, DUBOIS C, et al. Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation[C]//KDD 2013: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2013: 446-454.
[5] SALTON G, WONG A, YANG C S. A vector space model for automatic indexing[J]. Communications of the ACM, 1975, 18(11): 613-620.
[6] LEE Y-S, LO R, CHEN C-Y, et al. News topics categorization using latent Dirichlet allocation and sparse representation classifier[C]//Proceedings of the 2015 IEEE International Conference on Consumer Electronics. Piscataway, NJ: IEEE, 2015: 136-137.
[7] MORO S, CORTEZ P, RITA P. Business intelligence in banking: a literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation[J]. Expert Systems with Applications, 2015, 42(3): 1314-1324.
[8] ANOOP V S, PREM SANKAR C, ASHARAF S, et al. Generating and visualizing topic hierarchies from microblogs: an iterative latent Dirichlet allocation approach[C]//Proceedings of the 2015 International Conference on Advances in Computing, Communications and Informatics. Piscataway, NJ: IEEE, 2015: 824-828.
[9] LIU X, ZENG J, YANG X, et al. Scalable parallel EM algorithms for latent Dirichlet allocation in multi-core systems[C]//WWW 2015: Proceedings of the 24th International Conference on World Wide Web. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee, 2015: 669-679.
[10] DEEPAK N A, HARIHARAN R, SINHA U N. Cluster based human action recognition using latent Dirichlet allocation[C]//Proceedings of the 2013 International Conference on Circuits, Controls and Communications. Piscataway, NJ: IEEE, 2013: 1-4.
[11] DEERWESTER S, DUMAIS S T, FURNAS G W, et al. Indexing by latent semantic analysis[J]. Journal of the American Society for Information Science, 1990, 41(6): 391-407.
[12] HOFMANN T. Probabilistic latent semantic indexing[C]//SIGIR 1999: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 1999: 50-57.
[13] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation[J]. The Journal of Machine Learning Research, 2003, 3: 993-1022.
[14] 项亮. 推荐系统实战[M]. 北京:人民邮电出版社, 2012: 1-2.(XIANG L. Recommendation in Practice[M]. Beijing: Posts & Telecom Press, 2012: 1-2.)
[15] BELL R, KOREN Y, VOLINSKY C. Modeling relationships at multiple scales to improve accuracy of large recommender systems[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2007: 95-104.
[16] ONUMA K, TONG H, FALOUTSOS C. TANGENT: a novel, "surprise me", recommendation algorithm[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2009: 657-666.
[17] MUSTO C. Enhanced vector space models for content-based recommender systems[C]//Proceedings of the Fourth ACM Conference on Recommender Systems. New York: ACM, 2010: 361-364.
[18] SONG Y, ZHUANG Z, LI H, et al. Real-time automatic tag recommendation[C]//Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2008: 515-522.
[19] MASSA P, AVESANI P. Trust-aware collaborative filtering for recommender systems[C]//Proceedings of the OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2004, LNCS 3290. Berlin: Springer, 2004: 492-508.
[20] MASSA P, AVESANI P. Trust-aware recommender systems[C]//Proceedings of the 2007 ACM Conference on Recommender Systems. New York: ACM, 2007: 17-24.
[21] MA H, YANG H, LYU M R, et al. SoRec: social recommendation using probabilistic matrix factorization[C]//Proceedings of the 17th ACM Conference on Information and Knowledge Management. New York: ACM, 2008: 931-940.
[22] DONG X, WEI L, ZHU H, et al. An efficient privacy-preserving data-forwarding scheme for service-oriented vehicular Ad Hoc networks[J]. IEEE Transactions on Vehicular Technology, 2011, 60(2): 580-591.
[23] WEI X, CROFT W B. LDA-based document models for Ad Hoc retrieval[C]//Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2006: 178-185.
[24] HE B, AGRAWAL D P. An identity-based authentication and key establishment scheme for multi-operator maintained wireless mesh networks[C]//Proceedings of the 7th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems. Piscataway, NJ: IEEE, 2010: 71-78.
[25] 朱郁筱, 吕琳援. 推荐系统评价指标综述[J]. 电子科技大学学报, 2012, 41(2):164-175.(ZHU Y X, LYU L Y. Evaluation metrics for recommender systems[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(2): 164-175.)
[26] MANNING C D, RAGHAVAN P, SCHUTZE H. Introduction to Information Retrieval[M]. Cambridge: Cambridge University Press, 2008:76-80.
[27] LI Z, TIAN W, LI Y, et al. A more effective method for image representation: topic model based on latent Dirichlet allocation[C]//Proceedings of the 201514th International Conference on Computer-Aided Design and Computer Graphics. Piscataway, NJ: IEEE, 2015: 143-148.

基于LDA主题模型的移动应用相似度构建方法

Construction method of mobile application similarity matrix based on latent Dirichlet allocation topic model

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	蔡彪, 李蕊岑, 吴媛媛. 相似性特征对链路预测的影响与增强[J]. 计算机应用, 2021, 41(9): 2569-2577.
[2]	张成, 万源, 强浩鹏. 基于知识蒸馏的深度无监督离散跨模态哈希[J]. 计算机应用, 2021, 41(9): 2523-2531.
[3]	张洋, 江铭虎. 基于注意力机制的文本作者识别[J]. 计算机应用, 2021, 41(7): 1897-1901.
[4]	车冰倩, 周栋. 融合网络结构信息及文本内容的标签推荐方法[J]. 计算机应用, 2021, 41(4): 976-983.
[5]	杨丰瑞, 霍娜, 张许红, 韦巍. 基于注意力机制的主题扩展情感对话生成[J]. 计算机应用, 2021, 41(4): 1078-1083.
[6]	马晓梅, 何非. 基于改进遗传算法的标签印刷生产调度技术[J]. 计算机应用, 2021, 41(3): 860-866.
[7]	李伟强, 雷航, 张静玉, 王旭鹏. 基于标签分布学习的三维手部姿态估计[J]. 计算机应用, 2021, 41(2): 550-555.
[8]	闫钧华, 侯平, 张寅, 吕向阳, 马越, 王高飞. 基于多尺度多分类器卷积神经网络的混合失真类型判定方法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3178-3184.
[9]	杨威亚, 余正涛, 高盛祥, 宋燃. 基于跨语言神经主题模型的汉越新闻话题发现方法[J]. 计算机应用, 2021, 41(10): 2879-2884.
[10]	朱思淼, 魏世伟, 魏思恒, 余敦辉. 基于弹幕情感分析和主题模型的视频推荐算法[J]. 计算机应用, 2021, 41(10): 2813-2819.
[11]	张增辉, 姜高霞, 王文剑. 基于局部概率抽样的标签噪声过滤方法[J]. 计算机应用, 2021, 41(1): 67-73.
[12]	尹春勇, 章荪. 面向短文本情感分类的端到端对抗变分贝叶斯方法[J]. 计算机应用, 2020, 40(9): 2536-2542.
[13]	盛俊, 李斌, 陈崚. 基于模块度和标签传递的推荐算法[J]. 计算机应用, 2020, 40(9): 2606-2612.
[14]	尹春勇, 朱宇航. 基于垂直集成Tri-training的虚假评论检测模型[J]. 计算机应用, 2020, 40(8): 2194-2201.
[15]	田保军, 刘爽, 房建东. 融合主题信息和卷积神经网络的混合推荐算法[J]. 计算机应用, 2020, 40(7): 1901-1907.