摘要
为了提高传统的协同过滤推荐系统的性能,首次提出了考虑时序性的基于滚动时间窗的用户-项目-时间三维动态模型,并在此基础上研究了针对该模型的协同过滤推荐算法。该模型算法对不同时间的兴趣评分按时间序列处理,用户兴趣相似度由不同时间段的分量组合而成,提高了算法的时效性;进而推导出了该模型的增量算法,利用增量算法减少了计算相似度的时间复杂度,从而提高了算法的扩展性;最后设计了合理的实验,实验结果表明提出的三维动态模型及算法在命中率性能上优于传统的二维协同过滤推荐模型及算法。
For improving the performance of the traditional collaborative filtering recommender system, a dynamic user item-time thre^dimensional model based on rolling time windows was proposed, which considers the time sequence problem. Then a special collaborative filtering （CF） algorithm was explored to work with the model. The interest scores at different times are regarded differently according to the time sequence and the similarities between users are com- posed of components at different times, which increases the timeliness of the algorithm. In addition, the similarities can also be calculated quickly by an incremental formula deduced in this paper so as to improve the scalability of the algo- rithm At last, some reasonable experiments show that the model and algorithm presented in this paper outperform the traditional 2D collaborative filtering model and algorithm in terms of the hit rate.
出处
《计算机科学》
CSCD
北大核心
2013年第2期206-209,共4页
Computer Science
基金
国家高技术研究发展计划(863计划)项目(2011AA040605)资助
关键词
滚动时间窗
协同过滤
用户-项目-时间三维模型
推荐算法
时间序列
增量算法
Rolling time windows, Collaborative filtering, User-item-time 3D model, Recommender algorithm, Time sequences, Incremental algorithm
