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基于项目类别相似性的协同过滤推荐算法 被引量：21

A collaborative filtering recommendation algorithm based on item category similarity
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摘要 随着电子商务站点用户和商品项数量的不断增长,用户评分数据稀疏性问题成为基于项目的协同过滤推荐算法的瓶颈;文章提出了项目类别相似性的计算方法,并将项目类别相似性与传统的项目评分相似性进行加权组合,得到项目综合相似性,从而在提高最近邻居项目搜寻准确度的同时也缓解了数据稀疏性问题;实验结果表明,该算法能有效提高推荐质量。 With the increasing of quantity of users and goods in E-commerce websites, the sparse users rating data problem has been a bottleneck of the item-based collaborative filtering algorithm. To solve the problem, a computing method of item category similarity is proposed. Item category similarity and traditional item rating similarity have been synthesized to get synthetical item similarity, thus the accurate degree of searching nearest neighbor items has been improved and the sparse users rating data problem has been alleviated simultaneously. The experimental results show that the algorithm can efficiently improve recommendation quality.
作者 李聪 梁昌勇 董珂
机构地区 合肥工业大学管理学院 合肥工业大学电气与自动化工程学院
出处 《合肥工业大学学报（自然科学版）》 CAS CSCD 北大核心 2008年第3期360-363,共4页 Journal of Hefei University of Technology：Natural Science
基金 教育部重点资助项目(107067) 高校博士点基金资助项目(20050359006)
关键词 协同过滤 推荐算法 项目类别相似性 平均绝对偏差 collaborative filtering recommendation algorithm item category similarity mean absolute error
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献8

  • 1Schafer J B, Konstan J A, Riedl J. E-commerce recommendation applications[J]. Data Mining and Knowledge Discovery, 2001, 5(1/2):115--153.
  • 2Sarwar B M, Karypis G, Konstan J A, et al. Analysis of recommendation algorithms for E-commerce[C]//Proc of the 2nd ACM Conference on Electronic Commerce. New York: ACM Press, 2000:158--167.
  • 3Sarwar B, Karypis G, Konstan J, et al. Item-based collaborative filtering recommendation algorithms [C]//Proc of the 10th International Conference on World Wide Web. New York: ACM Press, 2001 : 285--295.
  • 4Aggarwal C C, Wolf J L, Wu K L, et al. Horting hatches an egg: a new graph-theoretic approach to collaborative filtering[C]//Proc of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 1999: 201--212.
  • 5邓爱林,左子叶,朱扬勇.基于项目聚类的协同过滤推荐算法[J].小型微型计算机系统,2004,25(9):1665-1670. 被引量：147
  • 6赵亮,胡乃静,张守志.个性化推荐算法设计[J].计算机研究与发展,2002,39(8):986-991. 被引量：140
  • 7张锋,常会友.使用BP神经网络缓解协同过滤推荐算法的稀疏性问题[J].计算机研究与发展,2006,43(4):667-672. 被引量：85
  • 8周军锋,汤显,郭景峰.一种优化的协同过滤推荐算法[J].计算机研究与发展,2004,41(10):1842-1847. 被引量：103

二级参考文献40

  • 1J Breese, D Hecherman, C Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In: Proc of the 14th Conf on Uncertainty in Artificial Intelligence (UAI98) . San Francisco,CA: Morgan Kaufmann, 1998. 43～52
  • 2B Sarwar, G Karypis, J Konstan, et al. Item-based collaborative filtering recommendation algorithms. In: Proc of the 10th Int'l World Wide Web Conf. New York: ACM Press, 2001. 285～295
  • 3A Dempster, N Laird, D Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 1977, 39(1): 1～38
  • 4B Thiesson, C Meek, D Chickering, et al. Learning mixture of DAG models. Microsoft Research, Tech Rep: MSR-TR-97-30,1997
  • 5B Sarwar, G Karypis, J Konstan, et al. Analysis of recommendation algorithms for E-commerce. In: Proc of the 2nd ACM Conf on Electronic Commerce. New York: ACM Press,2000. 158～167
  • 6J Wolf, C Aggarwal, K-L Wu, et al. Horting hatches an egg: A new graph-theoretic approach to collaborative filtering. In: Proc of the 5th ACM SIGKDD Int'l Conf on Knowledge Discovery and Data Mining. New York: ACM Press, 1999. 201～212
  • 7C C Aggarwal. On the effects of dimensionality reduction on high dimensional similarity search. In: Proc of the 20th ACM SIGMOD-SIGACT-SIGART Symp on Principles of Database Systems. New York: ACM Press, 2001. 256～266
  • 8B Kitts, D Freed. Cross-sell: A fast promotion-tunable customeritem recommendation method based on conditionally independent probabilities. In: Proc of ACM SIGKDD2000. New York: ACM Press, 2000. 437～446
  • 9Schafer J B, Konstan J A and Riedl J. Recommender systems in E-Commerce[C]. In: ACM Conference on Electronic Commerce(EC99), 1999, 158-166.
  • 10Breese J, Hecherman D and Kadie C. Empirical analysis of predictive algorithms for collaborative filtering[C]. In:Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence(UAI-98), 1998, 43-52.

共引文献397

同被引文献187

引证文献21

二级引证文献183

合肥工业大学学报（自然科学版）
2008年 第3期

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