Integrating improved weighted Slope One into collaborative filtering algorithm

WANG Jia-hua; TAN Guo-xin; ZHANG Wen-yuan; WANG Yang; YANG Guan-ci

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Integrating improved weighted Slope One into collaborative filtering algorithm

更新时间：2026-09-04

- Integrating improved weighted Slope One into collaborative filtering algorithm
- Vol. 37, Issue 4, Pages: 37-42(2020)
- 作者机构：
  
  1. 华中师范大学国家文化产业研究中心,湖北,武汉,430079
  2. 郑州银行博士后工作站,河南,郑州,450046
  3. 贵州大学现代制造技术教育部重点实验室机构,贵州,贵阳,550025
- 作者简介：
- 基金信息：
- DOI：
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- Published：2020
- 稿件说明：
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王家华, 谈国新, 张文元, 王阳, 杨观赐. 融合改进加权Slope One的协同过滤算法[J]. 微电子学与计算机, 2020, 37(4): 37-42.

WANG Jia-hua, TAN Guo-xin, ZHANG Wen-yuan, et al. Integrating improved weighted Slope One into collaborative filtering algorithm[J]. 2020, 37(4): 37-42.
王家华, 谈国新, 张文元, 王阳, 杨观赐. 融合改进加权Slope One的协同过滤算法[J]. 微电子学与计算机, 2020, 37(4): 37-42. DOI：

WANG Jia-hua, TAN Guo-xin, ZHANG Wen-yuan, et al. Integrating improved weighted Slope One into collaborative filtering algorithm[J]. 2020, 37(4): 37-42. DOI：

摘要

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.

Abstract

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 the problem that the backfill data is too single.Simulation experiments on movieslens-100k datatset show that the improved algorithm effectively reduces the MAE and RMSE

and improves the recommendation accuracy while alleviating the problem of data sparsity.

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references

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