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

个性化研究与多视角定位

下载PDF
导出
摘要 在简述个性化相关研究的基础上,重点分析了几种经典推荐方法的优缺点,并在分析其原因的基础上,针对个性化研究的未来提出了一个新的研究思路:从多角度进行用户兴趣的挖掘。
作者 郑自国 尹相杰 万桂怡
机构地区 山东建筑大学 山东大学
出处 《信息系统工程》 2010年第8期131-132,共2页
关键词 协同过滤 个性化推荐 多视角
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献5

  • 1曾春,邢春晓,周立柱.个性化服务技术综述[J].软件学报,2002,13(10):1952-1961. 被引量：396
  • 2Mladenic,D.Machine learning for better WebBrowsing[C].AAAI 2000 Spring Symposium Technical Reports on Adaptive User Interfaces.Menlo Park,CA:AAAI Press,2000:82-84.
  • 3Rucker,J.,Polanco,M.J.Siteseer.Personalized navigation for the web[J].Communications of the ACM,1997,40(3):73-75.
  • 4Konstan,J.,Miller,B.,Maltz,D.,et al.GroupLens.Applying collaborative filtering to usenet news[J].Communications of the ACM,1997,40(3):77-87.
  • 5Mobasher,B.,Cooley,R.,Srivastava,J.Automatic personalization based on Web usage mining[J].Communications of the ACM,2000,43(8):142-151.

二级参考文献41

  • 1Han, 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.
  • 2Schwab, 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.
  • 3Pretschner, A. Ontology based personalized search [MS. Thesis]. Lawrence, KS: University of Kansas, 1999.
  • 4Adomavicius, 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.
  • 5Balabanovic, M., Shoham, Y. Fab: content-based, collaborative recommendat ion. Communications of the ACM, 1997,40(3):66～72.
  • 6Sarwar, 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.
  • 7Sarwar, 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.
  • 8Breese, 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.
  • 9Aggarwal, 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.
  • 10Sarwar, B., Karypis, G., Konstan, J., et al. Item-Based collaborative fil tering recommendation algorithms. In: Shen, V.Y., Saito, N., eds. Proceedings of the 10th International World Wide Web Conference (WWW10). 2001. 285～295.

共引文献395

信息系统工程
2010年 第8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