摘要
随着数据的极端稀疏性,仅仅依赖于传统的协同过滤相似性的度量方法已无法取得精确的推荐结果。针对这一问题,提出基于用户特征属性和云模型的协同过滤算法。首先,算法利用云模型计算用户评分云相似性,结合用户打分偏好对原矩阵进行填充,在此基础上得到用户的评分云相似性;其次,再结合用户特征属性相似性通过加权因子计算用户的最终相似性,得到一种新的相似性度量方法;最后,得到算法的评分预测。实验结果表明,该方法能够提高推荐质量。
With the extreme sparsity of the data,traditional collaborative filtering similarity metrics are unable to obtain accurate recommendation results.In order to solve this problem,a collaborative filtering algorithm based on user feature and cloud model is proposed.Firstly,it takes advantage of the cloud model to calculate the similarity of user rating cloud,combines with user scoring preference to fill the original matrix,and then get user cloud similarity.Secondly,combining with user feature similarity and user cloud similarity,a new similarity measure method is proposed to calculate the final similarity by using weighting factor.Finally,the final rating prediction is obtained.The experimental results show that this approach can improve the recommended quality.
出处
《计算机工程与科学》
CSCD
北大核心
2014年第6期1172-1176,共5页
Computer Engineering & Science
基金
江西省教育厅科技项目(GJJ08283
GJJ11463)
江西省高等学校智能计算与网络测控技术重点实验室2012年资助项目
关键词
协同过滤
云模型
用户特征属性相似性
打分偏好
云相似性
collaborative filtering
cloud model
user characteristic attribute similarity
scoring preference
cloud similarity
