| 一种改进的协同过滤推荐算法 |
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| 引用本文： | 王茜,王均波.一种改进的协同过滤推荐算法[J].计算机科学,2010,37(6):226-228243. |
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| 作者姓名： | 王茜 王均波 |
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| 作者单位： | 重庆大学计算机学院,重庆,400044 |
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| 摘 要： | 传统的协同过滤算法在寻找最近邻居集合时没有考虑时间因素的影响,仅从用户或者项目单方面出发计算用户或者项目的相似性以产生推荐结果,也忽略了用户特征对推荐的影响.针对上述问题,引入时间遗忘函数、黏度函数、用户特征向量,对协同过滤算法寻找用户的最近邻居集合过程进行了改进,体现了时间效应、用户偏好程度和用户特征.采用MovieLens数据集进行了一系列对比实验,结果表明,改进后的算法能够明显提高推荐的准确度.
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| 关 键 词： | 协同过滤 时间效应 用户偏好度 用户特征向量 |
| 收稿时间： | 2009/7/14 0:00:00 |
| 修稿时间： | 2009/9/27 0:00:00 |
Improved Collaborative Filtering Recommendation Algorithm |
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| WANG Qian,WANG Jun-bo.Improved Collaborative Filtering Recommendation Algorithm[J].Computer Science,2010,37(6):226-228243. |
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| Authors: | WANG Qian WANG Jun-bo |
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| Affiliation: | (Department of Computer Science,Chongqing University,Chongqing 400044,China) |
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| Abstract: | When searching the nearest neighbor set, the traditional collaborative filtering algorithm ignores the impact of the time factor, only from the user or item takes into account the similarity of the user or item unilaterally, and ignores the impact of user characteristics on recommendation. Aiming to the above problems, we introduced the time forgotten function, resources viscosity function and the user feature vector, and improved the process of finding the user's nearest neighbor set, reflected the time effect, degree of user preferences and user characteristics, and tested this new methodology on data set got from MovicLens. I}hc results of experiment show that proposed method can improve the accuracy of the prediction. |
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| Keywords: | Collaborative filtering Time effect Uscr preference degree Uscr characteristic |
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