基于粗糙集规则提取的协同过滤推荐算法

任永功; 张云鹏; 张志鹏

doi:10.11959/j.issn.1000-436x.2020028

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基于粗糙集规则提取的协同过滤推荐算法

学术论文 | 更新时间：2024-06-05

- 基于粗糙集规则提取的协同过滤推荐算法
- Collaborative filtering recommendation algorithm based on rough set rule extraction
- 通信学报 2020年41卷第1期页码：76-83
- 作者机构：
  
  辽宁师范大学计算机与信息技术学院，辽宁大连 116000
- 作者简介：
  
  [ "任永功（1972- ），男，辽宁兴城人，博士，辽宁师范大学教授，主要研究方向为人工智能、数据挖掘等" ]
  [ "张云鹏（1993- ），男，辽宁沈阳人，辽宁师范大学硕士生，主要研究方向为数据挖掘、推荐系统等" ]
  [ "张志鹏（1988- ），男，河南安阳人，博士，辽宁师范大学讲师，主要研究方向为人工智能、大数据分析、推荐系统等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61976109);辽宁省自然科学基金资助项目(20180550542);大连市科技创新基金资助项目(2018J12GX047);大连市重点实验室专项基金资助项目
- DOI：10.11959/j.issn.1000-436x.2020028
  中图分类号： TP391
- 网络出版日期：2020-01，
  
  纸质出版日期：2020-01-25
- 稿件说明：
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任永功, 张云鹏, 张志鹏. 基于粗糙集规则提取的协同过滤推荐算法[J]. 通信学报, 2020,41(1):76-83.

Yonggong REN, Yunpeng ZHANG, Zhipeng ZHANG. Collaborative filtering recommendation algorithm based on rough set rule extraction[J]. Journal on communications, 2020, 41(1): 76-83.
任永功, 张云鹏, 张志鹏. 基于粗糙集规则提取的协同过滤推荐算法[J]. 通信学报, 2020,41(1):76-83. DOI： 10.11959/j.issn.1000-436x.2020028.

Yonggong REN, Yunpeng ZHANG, Zhipeng ZHANG. Collaborative filtering recommendation algorithm based on rough set rule extraction[J]. Journal on communications, 2020, 41(1): 76-83. DOI： 10.11959/j.issn.1000-436x.2020028.

摘要

基于现实推荐系统数据集非常稀疏，导致传统的协同过滤算法往往无法提供高质量推荐的问题，提出了一种基于粗糙集规则提取的协同过滤算法。首先利用用户/物品属性和用户-物品评分矩阵构建决策表，然后通过决策表约简算法得到每一条规则的核值，最后根据核值表的核值决策规则，完成所有决策规则的约简，从而实现对未评分的用户进行预测评分。实验结果表明，所提方法可以有效地缓解稀疏数据对协同过滤带来的负面影响，提高推荐结果的准确度。

Abstract

To address the problem that in a practical recommendation system (RS)

because of the datasets are often very sparse

the traditional collaborative filtering (CF) approach cannot provide recommendations with higher quality

a novel CF based on rough set rule extraction was proposed.Firstly

the attributes of user/item and the user-item rating matrix were used to construct a decision table.Then

the core value of each rule in the table was extracted through using the decision table reduction algorithm.Finally

according to the nuclear value decision rule of the core value table

the reductions of all decision rules were utilized to predict the rating scores of un-rated items.Experimental results suggest that the proposed approach can alleviate the data sparsity problem of CF

and provide recommendations with higher accuracy.

关键词

Keywords

references

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