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中文摘要：
基于用户的近期行为能够更好地反映其潜在的兴趣偏好的思想,提出了一种基于有限时间窗口的改进混合推荐算法.在标准数据集Netflix上的实验结果表明,只采用大约31.11%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4.22%,而推荐列表多样性可以提高13.74%.另外还发现新提出的算法适用于不同活跃程度的用户,这可以极大地降低大规模数据所引发的计算复杂性问题.
英文摘要：
Since the recent behaviors are more effective to capture the users’potential interests, an improved hybrid recommendation algorithm was proposed for making use of the partial recent information.The experimental results on the benchmark dataset Netflix indicate that by only adopting approximately 31.11% recent rating records,the accuracy can be improved by an average of 4.22%,and the diversity can be improved by 13.74%.Furthermore,it is found that the improved algorithm is suitable for the users with different level of activeness.The study is valuable in both theory and practice,and it could effectively handle the calculation complexity triggered by massive data.
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