摘要
研究了时间窗口对基于10种用户相似性指标的个性化推荐算法的影响。在标准数据集MovieLens上的实验结果表明,只采用大约12.56%的用户近期历史记录,所得到的推荐结果准确性可以平均提高27.17%,而推荐列表多样性可以平均提高3.28%,极大地降低大规模数据所带来的计算复杂性问题。
In this paper,we investigate the effect of the time window on the personalized recommendation algorithm based on ten similarity measures.The experimental results on the benchmark dataset MovieLens indicate that by only adapting approximately 12.56% recent rating records,the accuracy could be improved by an average of 27.17%,and the diversity could be improved by 3.28%.Our work is valuable in both theory and practice,and it could largely reduce the calculation complexity triggered by massive data.
出处
《复杂系统与复杂性科学》
EI
CSCD
北大核心
2015年第1期28-31,共4页
Complex Systems and Complexity Science
基金
国家自然科学基金(61374177
71371125
71171136)
上海市一流学科建设项目(XTKX2012)
关键词
个性化推荐算法
时间窗口
二部分网络
personalized recommendation algorithm
time window
bipartite network
