期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

协同过滤算法在推荐系统中的应用

Collaborative Filtering Algorithm in Recommendation Systems
下载PDF
导出
摘要 介绍了协同过滤算法,并对算法进行了改进,解决了用户稀疏的情况下传统算法的不足,同时通过引入评分阈值,显著提高了个性化协同过滤算法的推荐精度。 Collaborative filtering algorithm is introduced,and the algorithm is improved.It overcomes the drawbacks of conventional algorithms under the sparse user circumstances,and it remarkably raises the recommendation accuracy of personalized collaborative filtering algorithm through introducing the score threshold.
作者 胡炜
机构地区 江西理工大学信息工程学院
出处 《计算机时代》 2009年第11期16-17,20,共3页 Computer Era
关键词 推荐系统 推荐算法 协同过滤 协同过滤算法 recommendation system recommendation algorithm collaborative filtering collaborative filtering algorithm
分类号 TP274.2 [自动化与计算机技术—检测技术与自动化装置] TP311.5 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献2

二级参考文献42

  • 1韩家炜 坎伯.数据挖掘--概念与技术[M].北京：机械工业出版社,2001..
  • 2Han, E.H., Boley, D., Gini, M., et al. WebACE: a web agent for document c ategorization and exploration. In: Sycara, K.P., Wooldridge, M., eds. Proceeding s of the 2nd International Conference on Autonomous Agents. New York: ACM Press, 1998. 408～415.
  • 3Schwab, I., Pohl, W., Koychev, I. Learning to recommend from positive evi dence. In: Riecken, D., Benyon, D., Lieberman, H., eds. Proceedings of the Inter national Conference on Intelligent User Interfaces. New York: ACM Press, 2000. 2 41～247.
  • 4Pretschner, A. Ontology based personalized search [MS. Thesis]. Lawrence, KS: University of Kansas, 1999.
  • 5Adomavicius, G., Tuzhilin, A. User profiling in personalization applicati ons through rule discovery and validation. In: Lee, D., Schkolnick, M., Provost, F., et al., eds. Proceedings of the 5th International Conference on Data Mining and Knowledge Discovery. New York: ACM Press, 1999. 377～381.
  • 6Balabanovic, M., Shoham, Y. Fab: content-based, collaborative recommendat ion. Communications of the ACM, 1997,40(3):66～72.
  • 7Sarwar, B.M., Karypis, G., Konstan, J.A., et al. Application of dimension ality reduction in recommender system--a case study. In: Jhingran, A., Mason, J.M., Tygar, D., eds. Proceedings of the ACM WebKDD Workshop on Web Mining for E -Commerce. New York: ACM Press, 2000.
  • 8Sarwar, B.M., Karypis, G., Konstan, J.A., et al. Analysis of recommendati on algorithms for e-commerce. In: Proceedings of the ACM Conference on Electroni c Commerce. New York: ACM Press, 2000. 158～167.
  • 9Breese, J.S., Heckerman, D., Kadie, C. Empirical analysis of predictive a lgorithms for collaborative filtering. In: Cooper, G.F., Moral, S., eds. Proceed ings of the 14th Conference on Uncertainty in Artificial Intelligence. San Franc isco: Morgan Kaufmann Publishers, 1998. 43～52.
  • 10Aggarwal, C.C., Wolf, J.L., Wu, K., et al. Horting hatches an egg: a new raph-theoretic approach to collaborative filtering. In: Chaudhuri, S., Madigan, D., Fayyad, U., eds. Proceedings of the ACM International Conference on Knowledg e Discovery and Data Mining. New York: ACM Press, 1999. 201～212.

共引文献421

计算机时代
2009年 第11期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部