Collaborative filtering recommendation algorithm based on rough set rule extraction

Yonggong REN; Yunpeng ZHANG; Zhipeng ZHANG

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

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Collaborative filtering recommendation algorithm based on rough set rule extraction

Papers | 更新时间：2024-06-05

- Collaborative filtering recommendation algorithm based on rough set rule extraction
- Journal on Communications Vol. 41, Issue 1, Pages: 76-83(2020)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61976109);The Natural Science Foundation of Liaoning Province(20180550542);Dalian Science and Technology Innovation Fund(2018J12GX047);Dalian Key Laboratory Special Fund
- DOI：10.11959/j.issn.1000-436x.2020028
  CLC： TP391
- Online First：2020-01，
  
  Published：25 January 2020
- 稿件说明：
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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：

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.

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references

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