本体技术在个性化推荐系统中的应用研究

doi:10.3976/j.issn.1002-4026.2016.02.019

山东科学 ›› 2016, Vol. 29 ›› Issue (2): 101-105.doi: 10.3976/j.issn.1002-4026.2016.02.019

本体技术在个性化推荐系统中的应用研究

范忠勇¹，张志军²*，张鹏飞²

1.日照广播电视大学，山东日照 276826；2．山东建筑大学计算机科学与技术学院，山东济南 250101

收稿日期:2015-08-17 出版日期:2016-04-20 发布日期:2016-04-20
作者简介:范忠勇（1972-），男，硕士，研究方向为数据挖掘和人工智能。
基金资助:
山东省科技发展计划(2014GGX101011)；山东省高等学校科技计划（J12LN31, J13LN11，J14LN14）；济南市高校院所自主创新计划（201401214, 201303001）

Application of ontology technology in personalized recommendation system

FAN Zhongyong¹,ZHANG Zhijun2*,ZHANG Pengfei²

1. Rizhao Radio &TV University, Rizhao 276826, China;
2. School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China

Received:2015-08-17 Online:2016-04-20 Published:2016-04-20

摘要/Abstract

摘要：

针对个性化推荐系统中传统的用户建模技术的缺陷，本文将领域本体的理论与方法应用到电子商务个性化推荐中，设计了一种本体和服装信息相结合的个性化推荐模型。利用Protégé实现了服装知识本体的构建过程，在真实数据集上的实验结果验证了算法的可行性和有效性。该设计与已有的推荐算法相比，具有更高的预测准确度。

关键词: 本体, 网络爬虫, 个性化推荐, 协同过滤, 偏好预测

Abstract:

In view of the negatives of conventional user modeling technology in personalized recommendation system, we apply the theory and method of domain ontology to personalized recommendation of ecommerce, and design an ontology technology and clothing information combined recommendation model. We implement the construction procedure of clothing ontology with ontology editor tool Protégé. Feasibility and effectiveness of the model are verified on practical datasets. It has higher prediction accuracy, as compared with existing recommendation models.

Key words: ontology, personalized recommendation, web crawler, preference prediction, collaborative filtering

中图分类号:

TP391

范忠勇，张志军，张鹏飞. 本体技术在个性化推荐系统中的应用研究[J]. 山东科学, 2016, 29(2): 101-105.

FAN Zhongyong,ZHANG Zhijun,ZHANG Pengfei. Application of ontology technology in personalized recommendation system[J]. SHANDONG SCIENCE, 2016, 29(2): 101-105.

参考文献

［1］L L Y, MEDO M, YEUNG C H, et al. Recommender systems［J］. Physics Reports, 2012, 519(1): 1-49.
［2］ZHANG Z J, LIU H. Application and research of improved probability matrix factorization techniques in collaborative filtering［J］. Int J Control Autom, 2014, 7(8):79-92.
［3］ ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: A survey of the stateoftheart and possible extensions［J］. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
［4］JAHRER M, TSCHER A, LEGENSTEIN R. Combining predictions for accurate recommender systems［M］//Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining.New York, US：ACM, 2010: 693-702.
［5］ZHANG Z J, LIU H. Research on ontologybased literature retrieval model［J］. Computer Modelling and New Technologies, 2014,18(6):281-289.
［6］GRUBER T R. A translation approach to portable ontology specifications［J］. Knowledge acquisition, 1993, 5(2): 199-220.
［7］GUARINO N. Formal ontology in information systems［M］//Proceedings of the 1st International Conference.Amsterdam:IOS Press, 2008.
［8］MIDDLETON S E, SHADBOLT N R, de ROURE D C. Ontological user profiling in recommender systems［J］. ACM Transactions on Information Systems (TOIS), 2004, 22(1): 54-88.
［9］CANTADOR I, BELLOG N A, CASTELLS P. Ontologybased personalised and contextaware recommendations of news items［M］//Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent TechnologyVolume 01. Washington, DC, US:IEEE Computer Society, 2008: 562-565.
［10］BURKE R. Integrating knowledgebased and collaborativefiltering recommender systems［M］//Proceedings of the Workshop on AI and Electronic Commerce. 1999: 69-72.
［11］NOOR S, MARTINEZ K. Using social data as context for making recommendations: an ontology based approach［M］//Proceedings of the 1st Workshop on Context, Information and Ontologies. ACM, 2009.
［12］CASTELLS P, FERN NDEZ M, VALLET D, et al. Selftuning personalized information retrieval in an ontologybased framework［M］//On the Move to Meaningful Internet Systems 2005: OTM 2005 Workshops.Berlin:Springer , 2005: 977-986.
［13］李扬, 陈超, 祁麟, 等. 一种基于用户行为相似度的协同推荐算法［C］//第六届和谐人机环境联合学术会议 (HHME2010), 第 19 届全国多媒体学术会议 (NCMT2010), 第 6 届全国人机交互学术会议 (CHCI2010), 第 5 届全国普适计算学术会议 (PCC2010) 论文集. 2010.
［14］肖敏, 熊前兴. 基于项目语义相似度的协同过滤推荐算法［J］. 武汉理工大学学报, 2009, 31(3): 21-23.

[1]	李晨，朱世伟，赵燕清，于俊凤. 基于MapReduce的网络爬虫设计与实现[J]. 山东科学, 2015, 28(2): 101-107.
[2]	邢晓辉, 牟义军. 一种基于本体的信息检索方案[J]. J4, 2011, 24(2): 56-60.
[3]	刘君, 曹宝香. 基于本体的面向服务的属性访问控制模型[J]. J4, 2010, 23(6): 78-81.
[4]	宋士涛, 李新建, 周书旺. 基于本体与刻面相结合的构件检索方法研究[J]. J4, 2010, 23(5): 10-44.

本体技术在个性化推荐系统中的应用研究

Application of ontology technology in personalized recommendation system

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

Metrics

本文评价

推荐阅读 0