计算机技术与发展2024，Vol.34Issue(5)：170-174,5.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0056
基于降低数据稀疏度的协同过滤算法
Collaborative Filtering Algorithm Based on Reducing Data Sparsity
摘要
Abstract
Collaborative filtering algorithm is a common algorithm in recommendation systems,and its core idea is to mine user preferences through historical data and calculate similar neighbor items of objects for recommendation.However,the general real data has a serious data sparsity,and there are too few common scoring items between users or projects,which makes some traditional similarity al-gorithms inaccurate in calculation and low in recommendation accuracy.The traditional Slope One algorithm is inaccurate,but it has simple implementation and high operation efficiency,which can be used as sparse data pre-filling to improve the accuracy of similarity calculation.Therefore,we introduce a collaborative filtering algorithm based on reducing data sparsity,incorporating the Slope One algorithm.Firstly,hierarchical clustering is performed on the user rating data,and then the Weighted Slope One algorithm is used to predict and fill in some blank data of the high-sparsity dataset,thereby significantly reducing the data sparsity and improving the accuracy of Pearson's similarity calculation.Finally,the object attribute preference similarity is introduced for fusion.Validation is performed using the MovieLens 100 K dataset,and the results clearly show a reduction in the Mean Absolute Error(MAE),indicating an improvement in recommendation accuracy.It is validated that the proposed algorithm can enhance recommendation accuracy to some extent.关键词
协同过滤/数据稀疏度/加权Slope One/皮尔逊相似度/对象属性Key words
collaborative filtering/data sparsity/Weighted Slope One/Pearson similarity/object properties分类
信息技术与安全科学引用本文复制引用
徐文涛,王诚..基于降低数据稀疏度的协同过滤算法[J].计算机技术与发展,2024,34(5):170-174,5.基金项目
国家自然科学基金(61801240) （61801240）
