EM最优参数求解的概率粗糙集推荐算法
TP301; 推荐系统根据用户对项目的历史评分实施推荐,评分矩阵的稀疏性导致推荐的先验知识不足,降低推荐准确率.粗糙集理论能够利用不完备知识实施有效推理,从而提出了基于人口统计学的概率粗糙集推荐模型,使用概率粗糙集理论划分等价类,降低了评分矩阵稀疏性对推荐结果的影响.使用基于最大期望(expectation maximization,EM)思想的参数求解算法求解参数α和β的最优值,将Pawlak粗糙集的边界域分解到正域或负域中,提升推荐效果.实验结果表明,概率粗糙集模型能够有效提高在评分矩阵非常稀疏情况下的推荐准确率,其在MovieLens数据集上的推荐准确率最高达到71.42％,覆盖率指标最...
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| Published in | 计算机科学与探索 Vol. 10; no. 2; pp. 285 - 292 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
安徽大学计算机科学与技术学院,合肥230601
2016
六安职业技术学院信息与电子工程学院,安徽六安237158%安徽大学计算机科学与技术学院,合肥,230601%六安职业技术学院信息与电子工程学院,安徽六安,237158 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1673-9418 |
| DOI | 10.3778/j.issn.1673-9418.1506015 |
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| Abstract | TP301; 推荐系统根据用户对项目的历史评分实施推荐,评分矩阵的稀疏性导致推荐的先验知识不足,降低推荐准确率.粗糙集理论能够利用不完备知识实施有效推理,从而提出了基于人口统计学的概率粗糙集推荐模型,使用概率粗糙集理论划分等价类,降低了评分矩阵稀疏性对推荐结果的影响.使用基于最大期望(expectation maximization,EM)思想的参数求解算法求解参数α和β的最优值,将Pawlak粗糙集的边界域分解到正域或负域中,提升推荐效果.实验结果表明,概率粗糙集模型能够有效提高在评分矩阵非常稀疏情况下的推荐准确率,其在MovieLens数据集上的推荐准确率最高达到71.42％,覆盖率指标最高达到99.18％. |
|---|---|
| AbstractList | TP301; 推荐系统根据用户对项目的历史评分实施推荐,评分矩阵的稀疏性导致推荐的先验知识不足,降低推荐准确率.粗糙集理论能够利用不完备知识实施有效推理,从而提出了基于人口统计学的概率粗糙集推荐模型,使用概率粗糙集理论划分等价类,降低了评分矩阵稀疏性对推荐结果的影响.使用基于最大期望(expectation maximization,EM)思想的参数求解算法求解参数α和β的最优值,将Pawlak粗糙集的边界域分解到正域或负域中,提升推荐效果.实验结果表明,概率粗糙集模型能够有效提高在评分矩阵非常稀疏情况下的推荐准确率,其在MovieLens数据集上的推荐准确率最高达到71.42％,覆盖率指标最高达到99.18％. |
| Author | 钱付兰 王红 陈功平 张燕平 |
| AuthorAffiliation | 安徽大学计算机科学与技术学院,合肥230601;六安职业技术学院信息与电子工程学院,安徽六安237158%安徽大学计算机科学与技术学院,合肥,230601%六安职业技术学院信息与电子工程学院,安徽六安,237158 |
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| Author_FL | QIAN Fulan ZHANG Yanping CHEN Gongping WANG Hong |
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| DocumentTitle_FL | Recommendation Algorithm of Probability Rough Set Based on Optimal Parameters of EM |
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| Keywords | 最大期望(EM)算法 参数求解 expectation maximization (EM) algorithm 粗糙集 recommendation algorithm rough set solving paramaters 推荐算法 |
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| Publisher | 安徽大学计算机科学与技术学院,合肥230601 六安职业技术学院信息与电子工程学院,安徽六安237158%安徽大学计算机科学与技术学院,合肥,230601%六安职业技术学院信息与电子工程学院,安徽六安,237158 |
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| Title | EM最优参数求解的概率粗糙集推荐算法 |
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