基于粗糙集规则提取的协同过滤推荐算法

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

通信学报 ›› 2020, Vol. 41 ›› Issue (1): 76-83.doi: 10.11959/j.issn.1000-436x.2020028

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()

辽宁师范大学计算机与信息技术学院，辽宁大连 116000

修回日期:2019-12-15 出版日期:2020-01-25 发布日期:2020-02-11
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
基金资助:
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

Collaborative filtering recommendation algorithm based on rough set rule extraction

Yonggong REN,Yunpeng ZHANG,Zhipeng ZHANG()

School of computer and information technology,Liaoning Normal University,Dalian 116000,China

Revised:2019-12-15 Online:2020-01-25 Published:2020-02-11
Supported by:
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

摘要/Abstract

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。

关键词: 个性化推荐, 协同过滤, 粗糙集, 规则提取

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.

Key words: personalized recommendation, collaborative filtering, rough set, rule extraction

中图分类号:

TP391

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.

图/表 4

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推荐长度	方法			邻居数量
推荐长度	方法	20	30	40	50	60
	IBCF	0.289	0.289	0.236	0.237	0.222
4	MVCF	0.333	0.313	0.250	0.167	0.167
	RSCF	0.402	0.386	0.305	0.298	0.316
	RECF	0.500	0.375	0.313	0.350	0.341
	IBCF	0.250	0.222	0.216	0.193	0.186
6	MVCF	0.278	0.292	0.167	0.111	0.111
	RSCF	0.325	0.285	0.259	0.289	0.297
	RECF	0.333	0.292	0.277	0.315	0.300
	IBCF	0.213	0.191	0.203	0.188	0.185
8	MVCF	0.250	0.250	0.125	0.083	0.083
	RSCF	0.300	0.289	0.268	0.284	0.275
	RECF	0.365	0.333	0.307	0.295	0.303
	IBCF	0.209	0.160	0.157	0.165	0.154
10	MVCF	0.200	0.200	0.100	0.067	0.067
	RSCF	0.285	0.258	0.265	0.275	0.256
	RECF	0.300	0.267	0.233	0.300	0.280

推荐长度	方法			邻居数量
推荐长度	方法	20	30	40	50	60
	IBCF	0.313	0.333	0.281	0.255	0.236
4	MVCF	0.355	0.325	0.305	0.285	0.255
	RSCF	0.405	0.387	0.328	0.306	0.323
	RECF	0.487	0.395	0.333	0.325	0.375
	IBCF	0.235	0.267	0.233	0.222	0.225
6	MVCF	0.285	0.285	0.265	0.235	0.235
	RSCF	0.355	0.313	0.289	0.297	0.283
	RECF	0.375	0.325	0.295	0.315	0.295
	IBCF	0.215	0.181	0.215	0.193	0.203
8	MVCF	0.235	0.235	0.133	0.197	0.222
	RSCF	0.338	0.296	0.278	0.265	0.233
	RECF	0.365	0.333	0.307	0.295	0.303
	IBCF	0.213	0.178	0.186	0.197	0.195
10	MVCF	0.215	0.215	0.178	0.137	0.113
	RSCF	0.289	0.287	0.258	0.247	0.226
	RECF	0.333	0.300	0.287	0.296	0.273

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

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