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基于关联规则的电子商务推荐系统研究 被引量：8

RESEARCH ON RECOMMENDER SYSTEM BASED ON ASSOCIATION RULES
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摘要 电子商务网站可以使用推荐系统分析客户的消费偏好,向每个客户具有针对性地推荐商品.推荐系统在帮助了客户的同时也提高了顾客对商务活动的满意度.本文首先对基于关联规则的推荐系统的相关知识进行了讨论,提出基于项目支持度的关联规则推荐算法,并通过实验验证该算法的可行性.在此基础上对基于关联规则推荐系统的结构进行了研究. Recommender systems may be used to analyze the preference of customer, recommend product to targeted customer. This paper discusses the technique related to recommeder system based on association rules.Then the paper proposes MSApriori algorithm.Experiment results show that the algorithm is effective.Based on this,the structure of recommender system was designed.
作者 索琪 卢涛
机构地区 哈尔滨工业大学
出处 《哈尔滨师范大学自然科学学报》 CAS 2005年第2期50-53,共4页 Natural Science Journal of Harbin Normal University
关键词 关联规则 系统研究 电子商务网站 推荐系统 系统分析 商务活动 推荐算法 客户 满意度 支持度 Electronic commerce Recommender system Association rule Minimum item supports
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论] N94 [自然科学总论—系统科学]
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参考文献6

  • 1I Schafer, J. B. , Konstan, J. , and Riedl, J. , E-Commerce Recommendations Applications. Journal of Data Mining and Knowledge Discovery, January-April 2001, Vol 5, Issue 1-2,pp.115-153.
  • 2Schafer, J. B. , Konstan, J. , and Riedl, J. Recommender Systems in E-Commerce. In Proceedings of ACM E-Commerce 1999 conference. 1999.
  • 3Sarwar, B. , Karypis, G. , Konstan, J. , Analysis of Recommendation Algorithms for E-Commerce. ACM Conference on Electronic Commerce, 2000.
  • 4Bing Liu, Wynne Hsu, Yiming Ma. Mining Association Rules with Mutiple Minimum Supports. ACM SIGKDD International Conference on Konwledge Discovery&Data Mining, 1999.
  • 5张锋,常会友,衣杨.基于规则的电子商务推荐系统模型和实现[J].计算机集成制造系统,2004,10(8):898-902. 被引量：11
  • 6赵耀,薛贵荣.Web Service下的商品推荐系统的研究与实现[J].临沂师范学院学报,2003,25(6):131-134. 被引量：3

二级参考文献18

  • 1SCHAFER J B ,KONSTAN J A,RIEDL J. E-commerce recommendation applications[J]. Data Mining and Knowledge Discovery, 2001,5(1/2): 115 - 153.
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  • 7ANSARI S,KOHAVI L,MASON Z,et al. Integrating e-commerce and data mining: architecture and challenges[A]. Proceedings of the 2001 IEEE International Conference on Data Mining[C]. California, USA: IEEE Computer Society Press, 2001.27-34.
  • 8MOONEY R J, ROY L. Content-based book recommending using learning for text categorization[A]. Proceedings of 5th ACM Conference on Digital Libraries[C]. New York, USA:ACM Press,2000. 195-204.
  • 9KARYPIS G. Evaluation of item-based top-n recommendation algorithms[A]. Proceedings of the 10th International Conference on Information and Knowledge Management[C].NewYork,USA:ACM Press,2001. 247- 254.
  • 10CHEE S H S, HAN J,WANG K. RecTree:an efficient collaborative filtering method[J]. Lecture Notes in Computer Science,2001,2114: 141- 151.

共引文献11

同被引文献44

引证文献8

二级引证文献36

哈尔滨师范大学自然科学学报
2005年 第2期

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