基于信息熵协同过滤算法

基于信息熵的协同过滤算法汇报人：张佳闽南师范大学计算机学院2021/7/8预备知识1主要内容基于用户的算法2基于信息熵的协同过滤算法3实验结果4参考文献52021/7/8协同过滤思想:根据用户过去的浏览记录，购买记录，评分记录及其主页标注等信息，分析用户潜在的兴趣偏好，以给用户提供个性化的推荐服务。例1：

the

mendation

of

movies

in

MTime2021/7/8国内外研究现状推荐系统二部图 混合过滤 基于内容 协同过滤基于模型 基于内存基于项目基于用户2021/7/8存在的问题2021/7/8可拓展性（Scalability）：当数据规模逐渐增大时，可能导致算法速度急剧下降，无法及时产生推荐。冷启动（Coldstart）：对于新加入的系统的项目或用户，由于缺乏评价资源，没办法进行推荐。稀疏性（Datasparsity）：收集到的用户数据有限，系统的性能和准确性较低。2021/7/8预备知识1主要内容基于用户的算法2基于信息熵的协同过滤算法3实验结果4参考文献5基于用户的算法2021/7/8基于用户的算法2021/7/8基于用户的算法2021/7/82021/7/8预备知识1主要内容基于用户的算法2基于信息熵的协同过滤算法3实验结果4参考文献5假设间隔2021/7/8模型构建2021/7/8模型构建2021/7/82021/7/8预备知识1主要内容基于用户的算法2基于信息熵的协同过滤算法3实验结果4参考文献5数据集及度量指标2021/7/8数据集（数据可在下载）：选用公开的MovieLens数据集（ML数据集）和HetRec2011-MovieLens数据集（HML数据集）对算法有效性进行验证。其中，ML数据集包含943个用户在1682个电影上的100000条评分记录，每个用户至少有20条评分记录，评分矩阵的稀疏等级为0.9370。鉴于HML数据集中评分记录过多，随机选择了其中的404个用户在1300个电影上的39259条评分记录，每个用户至少有3条评分记录，评分矩阵的稀疏等级为0.9254。度量指标：采用推荐算法中常见的平均绝对偏差(Mean实验结果预测结果优化用户间的相似性大小和信息熵差异对推荐结果的影响2021/7/8实验结果算法比较2021/7/8［1］ADOMAVICIUSG,TUZHILIN

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