期刊论文

Integrating improved weighted Slope One into collaborative filtering algorithm

中文

微电子学与计算机

1000-7180

2020

37

4

37-42

10.19304/j.cnki.issn1000-7180.2020.04.007

国家自然科学基金项目(61863005) 贵州省科技计划项目(黔科合平台人才[2018]5702,黔科合支撑[2019]2814,黔科合平台人才[2018]5781号) 湖北省技术创新专项重大项目(2018AAA069)。

本校为第一机构

针对传统协同过滤算法中存在的数据稀疏性问题,提出一种融合改进加权Slope One的协同过滤算法.该算法首先使用改进后的Slope One算法计算出的评分预测值,对初始的用户-评分矩阵进行有效填充,然后在新的评分矩阵上通过基于内存的协同过滤算法进行预测与推荐.经改进Slope One算法填充后的矩阵不仅大大降低了原始评分矩阵的稀疏性,同时也避免了回填数据过于单一的问题.在MovieLens-100k数据集上对文中算法进行仿真实验,结果表明,改进算法有效降低了MAE和RMSE,在提高推荐准确度的同时也缓解了数据稀疏性的问题.

Aiming at the problem of data sparsity in traditional collaborative filtering algorithm,this paper proposed a collaborative filtering algorithm combined with improved weighted Slope One.The algorithm firstly used the score prediction value calculated by the improved slope one algorithm to effectively fill in the initial user-score matrix,and then made prediction and recommendation through the memory-based collaborative filtering algorithm on the new score matrix.The matrix filled by the improved Slope One algorithm not only greatly reduced the sparsity of the scoring matrix,but also avoided th...