| 基于滚动时间窗的动态协同过滤推荐模型及算法 |
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| 引用本文： | 沈键,杨煌普.基于滚动时间窗的动态协同过滤推荐模型及算法[J].计算机科学,2013,40(2):206-209. |
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| 作者姓名： | 沈键 杨煌普 |
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| 作者单位： | (上海交通大学自动化系系统控制与信息处理教育部重点实验室 上海 200240) |
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| 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一项目一时间三
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。
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| 关 键 词： | 滚动时间窗，协同过滤，用户一项目一时间三维模型，推荐算法，时间序列，增量算法 |
Dynamic Collaborative Filtering Recommender Model Based on Rolling Time Windows and its Algorithm |
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| Abstract: | For improving the performance of the traditional collaborative filtering recommender system, a dynamic user-
item-time three-dimensional model based on rolling time windows was proposed, which considers the time seduence
problem. I}hen a special collaborative filtering (CF) algorithm was explored to work with the model. 1}he interest scores
at different times arc regarded differently according to the time sequence and the similarities between users arc com-
posed of components at different times,which increases the timeliness of the algorithm. In addition, the similarities can
also be calculated duickly 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. |
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| Keywords: | Keywords Rolling time windows Collaborative filtering User-item-time 3D model Recommender algorithm Time se-
qucnccs Incrcmcntal algorithm |
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