Recommendation rating prediction algorithm based on user interest concept lattice reduction

doi:10.11772/j.issn.1001-9081.2022121839

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (11): 3340-3345.DOI: 10.11772/j.issn.1001-9081.2022121839

• National Open Distributed and Parallel Computing Conference 2022 (DPCS 2022） • Previous Articles Next Articles

Recommendation rating prediction algorithm based on user interest concept lattice reduction

Xuejian ZHAO¹(), Hao LI², Haotian TANG²

^1.Technology and Application Engineering Center of Postal Big Data （Nanjing University of Posts and Telecommunications），Nanjing Jiangsu 210003，China
^2.School of Modern Posts，Nanjing University of Posts and Telecommunications，Nanjing Jiangsu 210003，China

Received:2022-12-07 Revised:2023-01-18 Accepted:2023-02-01 Online:2023-11-14 Published:2023-11-10
Contact: Xuejian ZHAO
About author:ZHAO Xuejian， born in 1982， Ph. D.， associate professor. His research interests include data mining， wireless sensor network.
LI Hao， born in 1999， M. S. candidate. His research interests include data mining.
TANG Haotian， born in 2001， M. S. candidate. His research interests include data mining.
Supported by:
National Natural Science Foundation of China(61672299);China Postdoctoral Science Foundation(2018M640509)

基于用户兴趣概念格约简的推荐评分预测算法

赵学健¹(), 李豪², 唐浩天²

^1.邮政大数据技术与应用工程中心（南京邮电大学），南京 210003
^2.南京邮电大学现代邮政学院，南京 210003

通讯作者: 赵学健
作者简介:赵学健（1982—），男，山东临沂人，副教授，博士，主要研究方向：数据挖掘、无线传感器网络 zhaoxj@njupt.edu.cn
李豪（1999—），男，江苏扬州人，硕士研究生，主要研究方向：数据挖掘
唐浩天（2001—），男，四川阿坝人，硕士研究生，主要研究方向：数据挖掘。
基金资助:
国家自然科学基金资助项目(61672299);中国博士后科学基金资助项目(2018M640509)

Abstract

Abstract:

The performance of the recommendation systems is restricted by data sparsity， and the accuracy of prediction can be effectively improved by reasonably filling the missing values in the rating matrix. Therefore， a new algorithm named Recommendation Rating Prediction based on Concept Lattice Reduction （RRP-CLR） was proposed. RRP-CLR algorithm was composed of nearest neighbor selection module and rating prediction module， which were respectively responsible for generating reduced nearest neighbor set and realizing rating prediction and recommendation. In the nearest neighbor selection module， the user rating matrix was transformed into a binary matrix， which was regarded as the user interest formal background. Then the formal background reduction rules and concept lattice redundancy concept deletion rules were proposed to improve the efficiency of generating reduced nearest neighbors. In the rating prediction module， a new user similarity calculation method was proposed to eliminate the impact of rating deviations caused by user’s subjective factors on similarity calculation. When the number of common rating items of two users was less than a specific threshold， the similarity was scaled appropriately to make the similarity between users more consistent with the real situation. Experimental results show that compared with PC?UCF （User-based Collaborative Filtering recommendation algorithm based on Pearson Coefficient） and RRP-UICL （Recommendation Rating Prediction method based on User Interest Concept Lattice）， RRP-CLR algorithm has smaller Mean Absolute Error （MAE） and Root Mean Squared Error （RMSE）， and better rating prediction accuracy and stability.

Key words: recommendation system, rating prediction, concept lattice, sparsity, reduced nearest neighbor

摘要：

数据稀疏性制约了推荐系统的性能，而合理填充评分矩阵中的缺失值可以有效提升预测的准确性。因此，提出一种基于用户兴趣概念格约简的推荐评分预测（RRP-CLR）算法。该算法包含近邻选择和评分预测两个模块，分别负责生成精简最近邻集合和实现评分预测及推荐。近邻选择模块将用户评分矩阵转化为二进制矩阵后作为用户兴趣形式背景，提出了形式背景约简规则和概念格冗余概念删除规则，以提高生成精简最近邻的效率；在评分预测模块利用新提出的用户相似度计算方法，消除用户主观因素造成的评分差异对相似度计算的影响，而且当两个用户共同评分项目数小于特定阈值时，适当缩放相似度，使用户间的相似度与真实情况更吻合。实验结果表明，与使用皮尔逊相关系数的基于用户的协同过滤推荐算法（PC-UCF）及基于用户兴趣概念格的推荐评分预测方法（RRP-UICL）相比，RRP-CLR算法的平均绝对误差（MAE）和均方根误差（RMSE）更小，具有更好的评分预测准确率和稳定性。

关键词: 推荐系统, 评分预测, 概念格, 稀疏性, 精简最近邻

CLC Number:

TP391

Xuejian ZHAO, Hao LI, Haotian TANG. Recommendation rating prediction algorithm based on user interest concept lattice reduction[J]. Journal of Computer Applications, 2023, 43(11): 3340-3345.

赵学健, 李豪, 唐浩天. 基于用户兴趣概念格约简的推荐评分预测算法[J]. 《计算机应用》唯一官方网站, 2023, 43(11): 3340-3345.

Figures/Tables 6

Tab. 1 User rating matrix SRM

用户	$i 1$	$i 2$	$i 3$	$i 4$	$i 5$	$i 6$	$i 7$	$i 8$	$i 9$
$u 1$		2		3		5		1
$u 2$	4		3		5		3		2
$u 3$		2		2		5
$u 4$		2						1	5
$u 5$	3	3	2	4			4		4
$u 6$	5		1	5			5	1
$u 7$	3				4	4			3

Tab. 1 User rating matrix SRM

用户	$i 1$	$i 2$	$i 3$	$i 4$	$i 5$	$i 6$	$i 7$	$i 8$	$i 9$
$u 1$		2		3		5		1
$u 2$	4		3		5		3		2
$u 3$		2		2		5
$u 4$		2						1	5
$u 5$	3	3	2	4			4		4
$u 6$	5		1	5			5	1
$u 7$	3				4	4			3

Tab. 2 Binary matrix SBM

用户	$i 1$	$i 2$	$i 3$	$i 4$	$i 5$	$i 6$	$i 7$	$i 8$	$i 9$
$u 1$	0	1	0	1	0	1	0	1	0
$u 2$	1	0	1	0	1	0	1	1	0
$u 3$	0	1	0	1	0	1	0	0	0
$u 4$	0	1	0	0	0	0	0	1	1
$u 5$	1	1	1	1	1	0	1	0	1
$u 6$	1	0	1	1	0	0	1	1	0
$u 7$	1	0	0	0	1	1	0	0	1

Tab. 2 Binary matrix SBM

用户	$i 1$	$i 2$	$i 3$	$i 4$	$i 5$	$i 6$	$i 7$	$i 8$	$i 9$
$u 1$	0	1	0	1	0	1	0	1	0
$u 2$	1	0	1	0	1	0	1	1	0
$u 3$	0	1	0	1	0	1	0	0	0
$u 4$	0	1	0	0	0	0	0	1	1
$u 5$	1	1	1	1	1	0	1	0	1
$u 6$	1	0	1	1	0	0	1	1	0
$u 7$	1	0	0	0	1	1	0	0	1

Fig. 1 Concept lattice CL

Fig. 2 Reduced concept lattice CL'

Fig. 3 Comparative analysis of MAE

Fig. 4 Comparative analysis of RMSE

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Recommendation rating prediction algorithm based on user interest concept lattice reduction

基于用户兴趣概念格约简的推荐评分预测算法

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