文章摘要
孙克雷,王琰.基于用户特征和时间权重的协同过滤算法[J].安徽建筑大学学报,2018,26(1):55-60
基于用户特征和时间权重的协同过滤算法
Collaborative Filtering Algorithm Based on User Feature and Time Weights
  
DOI:10.11921/j.issn.2095-8382.20180111
中文关键词: 最近邻居  用户特征  时间权重  兴趣迁移  协同过滤算法
英文关键词: Nearest neighbor  user characteristics  time weight  interest migration  collaborative filtering algorithm
基金项目:国家自然科学基金面上项目(51504010)
作者单位
孙克雷 安徽理工大学 计算机科学与工程学院淮南 232001 
王琰 安徽理工大学 计算机科学与工程学院淮南 232001 
摘要点击次数: 7517
全文下载次数: 0
中文摘要:
      为了解决传统算法中寻找最近邻居不准确和用户兴趣随时间变化而迁移的问题,提出一种基于用户特征和时间权重的协同过滤算法。文中首先把Movie Lens数据集中用户特征信息数字化,求出用户特征相似性,将其加入到修正的余弦公式中,得到一种新的用户相似度,以找到更加准确的最近邻居集;然后通过引入时间函数来反应用户的兴趣迁移,再根据预测评分公式来获得更加准确的预测评分;最后给用户生成一个较可靠的推荐结果。实验结果表明,该方法取得了较好的效果且平均绝对误差(MAE)值达到72.57%。
英文摘要:
      In order to solve the problem of user interest migration and inaccurate recommendation of traditional cooperative filtering algorithm, this paper proposes a collaborative filtering algorithm based on user characteristics and time weight. The algorithm first digitizes the user characteristic information of the MovieLens data set and obtains the user characteristic similarity. Then it adds to the modified cosine formula to get a new user similarity , finding a more accurate nearest neighbor set. Followed by the introduction of a time function to reflect the user's interest migration, which gives the only non-linearly decreasing weight of each score to obtain a more accurate predictive score. And finally produce a more reliable recommendation list. The experimental results show that the method has achieved good results with an average absolute error (MAE) of 72.57%.
查看全文   查看/发表评论  下载PDF阅读器
关闭

分享按钮