摘要
针对当前推荐系统中所面临数据稀疏、冷启动、时效性和隐私保护等问题,提出一种基于密度权重的隐私聚类和改进相似度的协同过滤推荐算法。该方法结合了差分隐私保护聚类与改进的相似度的协同过滤推荐算法,旨在提高推荐系统的精准度,同时确保用户数据的隐私安全。通过数据预处理构建用户-项目评分矩阵,并运用Weight Slope One算法智能填充空值,使用DWDPK-medoids隐私聚类算法对矩阵进行精确聚类,融合时间因素和用户兴趣偏好因素,改变相似度的计算,从而提高推荐相关性,最后预测目标用户对项目的评分。在MovieLens数据集,通过和当前学者提出的5种隐私推荐算法进行对比实验验证,该算法在评价指标均方根误差(root mean squared error,RMSE)和平均绝对误差(mean absolute error,MAE)上均有所降低,表明所提方法在一定程度上解决了数据稀疏、冷启动和时效性等问题,并在保护用户隐私的基础上提升了推荐准确性。
Aiming at the problems of sparse data,cold start,timeliness and privacy protection in current recommendation systems,a collaborative filtering recommendation algorithm based on density weight and improved similarity was proposed.The collaborative filtering recommendation algorithm,which combines differential privacy protection clustering and improved similarity,aims to improve the accuracy of the recommendation system and ensure the privacy security of user data.The user-project score matrix was constructed through data pre-processing,and the Weight Slope One algorithm was used to fill empty values in an intelligent way.The DWDPK-medoids privacy clustering algorithm was used to cluster the matrix accurately,and the time factor and user interest preference factors were integrated to change the calculation of similarity,thus improving the relevance of recommendation.Finally,the target user's rating of the project was predicted.Comparative experiments were conducted on the MovieLens dataset against five privacy recommendation algorithms proposed by current scholars validate the efficacy of the proposed algorithm,showing reductions in evaluation metrics such as root mean squared error(RMSE) and mean absolute error(MAE).This indicates that the method partially addresses issues such as data sparsity,cold start,and timeliness,while enhancing recommendation accuracy on the basis of protecting user privacy.
作者
王圣节
张庆红
WANG Sheng-jie;ZHANG Qing-hong(College of Statistics and Data Science,Xinjiang University of Finance and Economics,Urumqi 830012,China)
出处
《科学技术与工程》
北大核心
2024年第29期12623-12630,共8页
Science Technology and Engineering
基金
国家自然科学基金(72164034)。
关键词
推荐系统
隐私保护
聚类
协同过滤
相似度计算
recommender systems
privacy preservation
clustering
collaborative filtering
similarity computation
