Construction and recommendation application of expert communities based on user-centric approach

doi:10.11772/j.issn.1001-9081.2025050639

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (5): 1490-1498.DOI: 10.11772/j.issn.1001-9081.2025050639

• Data science and technology • Previous Articles

Construction and recommendation application of expert communities based on user-centric approach

Shijie YANG¹^,², Zhonghui LIU¹^,², Fan MIN¹^,²^,³()

^1.School of Computer Science and Software Engineering，Southwest Petroleum University，Chengdu Sichuan 610500，China
^2.Institute for Artificial Intelligence，Southwest Petroleum University，Chengdu Sichuan 610500，China
^3.Lab of Machine Learning，Southwest Petroleum University，Chengdu Sichuan 610500，China

Received:2025-06-10 Revised:2025-07-15 Accepted:2025-07-20 Online:2025-08-01 Published:2026-05-10
Contact: Fan MIN
About author:YANG Shijie， born in 1997， M. S. candidate. His research interests include formal concept analysis， network formal context.
LIU Zhonghui， born in 1980， M. S.， professor. Her research interests include machine learning， formal concept analysis， rough set.
Supported by:
National Natural Science Foundation of China(61976245);Special Project of Central Government Guiding Local Science and Technology Development （Targeted Project）(2021ZYD0003);University-Science and Technology Strategic Cooperation Project of Nanchong City(23XNSYSX0062)

基于用户中心的专家社区构建及推荐应用

杨仕杰¹^,², 刘忠慧¹^,², 闵帆¹^,²^,³()

^1.西南石油大学计算机与软件学院，成都 610500
^2.西南石油大学人工智能研究院，成都 610500
^3.西南石油大学机器学习研究中心，成都 610500

通讯作者: 闵帆
作者简介:杨仕杰（1997—），男，四川广元人，硕士研究生，主要研究方向：形式概念分析、网络形式背景
刘忠慧（1980—），女，四川南充人，教授，硕士，CCF会员，主要研究方向：机器学习、形式概念分析、粗糙集
基金资助:
国家自然科学基金资助项目(61976245);中央引导地方科技发展专项(2021ZYD0003);南充市校科技战略合作项目(23XNSYSX0062);南充市校科技战略合作项目(23XNSYJG0054)

Abstract

Abstract:

To address the issues of redundant recommendations and high time complexity in community-based recommendation methods under network formal contexts， a user-centric Expert Community Construction Algorithm （ECCA） and an Expert Community-Based Recommendation Algorithm （ECBRA） were proposed. Firstly， a screening method for expert nodes was determined by defining comprehensive node influence based on both network structure and positive rating； nodes with stronger comprehensive influence were selected as expert nodes. Secondly， the expert community for each user was constructed， consisting of the user node and all its adjacent expert nodes， ensuring that each user had an independent expert community. Finally， recommendations were made based on expert communities. Combined with user preferences， dynamic and static recommendation confidence thresholds were designed， and their sum was served as the recommendation confidence threshold. Personalized recommendation for each user was generated by calculating the recommendation confidence. Comparison results with the Group Recommendation Algorithm based on Weak-concept Similarity （GRAWS） showed that ECBRA's total time consumption was only 0.1% of GRAWS's， with no recommendation redundancy. Compared to classic collaborative filtering algorithms， including k-Nearest Neighbor （kNN） and Item-Based Collaborative Filtering （IBCF）， as well as Concept Set Based Recommendation （CSBR）， Concept Set based-Personalized Recommendation algorithm （CSPR）， and the combination of GreConD and kNN （GreConD-kNN） algorithms on 9 real datasets， ECBRA achieved better performance. Specifically， on the Netflix2 dataset， compared with CSPR， ECBRA had recall increased by 63.3%， F1-score increased by 13.9%； compared with the kNN algorithm， ECBRA had F1-score increased by 62.7%. Overall， ECBRA offers low redundancy and low time complexity.

Key words: network formal context, expert community, static confidence threshold, dynamic confidence threshold, recommendation system

摘要：

针对网络形式背景下基于社区推荐的方法会造成冗余推荐、时间复杂度过高的问题，提出一种以用户为中心的专家社区构建算法（ECCA）和基于专家社区的推荐算法（ECBRA）。首先，确定专家节点的筛选办法，设计由网络结构和正向评分共同构成的节点综合影响力，将综合影响力较大的节点选为专家节点；其次，构建每个用户的专家社区，由用户节点与它邻接的所有专家节点共同组建专家社区，保障所有用户均有自己独立的专家社区；最后，基于专家社区进行推荐，结合用户偏好设计动态和静态推荐置信度阈值，两阈值相加可得到推荐置信度阈值，再通过推荐置信度计算实现针对每个用户的个性化推荐。与基于弱概念相似度的组推荐算法（GRAWS）的比较结果表明，ECBRA综合用时仅为GRAWS的0.1%，无推荐冗余；在9个真实数据集与经典的协同过滤算法k最近邻（kNN）、基于项目的协同过滤（IBCF），以及基于概念集合的推荐（CSBR）算法、基于概念集的个性化推荐算法（CSPR）、结合形式概念分析与kNN的推荐（GreConD-kNN）算法的对比结果也表明，ECBRA效果较好，特别是在Netflix2数据集上，比CSPR算法的召回率高63.3%、F1值高13.9%，比kNN算法的F1值高62.7%。整体而言，ECBRA推荐冗余少，且时间复杂度低。

关键词: 网络形式背景, 专家社区, 静态置信度阈值, 动态置信度阈值, 推荐系统

CLC Number:

TP181

Shijie YANG, Zhonghui LIU, Fan MIN. Construction and recommendation application of expert communities based on user-centric approach[J]. Journal of Computer Applications, 2026, 46(5): 1490-1498.

杨仕杰, 刘忠慧, 闵帆. 基于用户中心的专家社区构建及推荐应用[J]. 《计算机应用》唯一官方网站, 2026, 46(5): 1490-1498.

Figures/Tables 14

Tab. 1 Network formal context

$O$	$M 1$				…	$M k$				$A$
$O$	$x 0$	$x 1$	…	$x n$	…	$x 0$	$x 1$	…	$x n$	$a 0$	$a 1$	…	$a n$
$x 0$	0	0	…	0	…	1	1	…	0	0	1	…	1
$x 1$	0	0	…	0	…	1	1	…	0	0	0	…	0
︙	︙	︙	︙	︙		︙	︙	︙	︙	︙	︙	︙	︙
$x n$	0	0	…	0	…	1	1	…	0	0	0	…	0

Tab. 1 Network formal context

$O$	$M 1$				…	$M k$				$A$
$O$	$x 0$	$x 1$	…	$x n$	…	$x 0$	$x 1$	…	$x n$	$a 0$	$a 1$	…	$a n$
$x 0$	0	0	…	0	…	1	1	…	0	0	1	…	1
$x 1$	0	0	…	0	…	1	1	…	0	0	0	…	0
︙	︙	︙	︙	︙		︙	︙	︙	︙	︙	︙	︙	︙
$x n$	0	0	…	0	…	1	1	…	0	0	0	…	0

Fig.1 Expert community examples

Fig.2 Overall framework of proposed algorithm

Tab. 2 Example of first-order network formal context

O	$M 1$										$A$
O	$x 0$	$x 1$	$x 2$	$x 3$	$x 4$	$x 5$	$x 6$	$x 7$	$x 8$	$x 9$	$a 0$	$a 1$	$a 2$	$a 3$	$a 4$	$a 5$	$a 6$	$a 7$	$a 8$
$x 0$	0	0	0	0	1	1	1	1	0	1	1	1	0	1	1	1	1	1	1
$x 1$	0	0	0	1	0	1	0	0	1	0	0	0	1	0	1	0	0	0	0
$x 2$	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0
$x 3$	0	1	1	0	0	0	1	1	0	0	1	0	0	0	0	1	1	0	0
$x 4$	1	0	0	0	0	0	1	1	0	0	1	0	1	1	0	1	0	0	0
$x 5$	1	1	0	0	0	0	1	0	1	1	1	0	0	0	1	0	1	0	1
$x 6$	1	0	0	1	1	1	0	1	1	1	0	0	1	0	0	0	1	1	1
$x 7$	1	0	0	1	1	0	1	0	0	0	0	0	1	1	0	0	1	0	0
$x 8$	0	1	0	0	0	1	1	0	0	1	0	1	0	1	0	1	1	0	0
$x 9$	1	0	0	0	0	1	1	0	1	0	1	0	1	1	0	1	1	0	1

Tab. 2 Example of first-order network formal context

O	$M 1$										$A$
O	$x 0$	$x 1$	$x 2$	$x 3$	$x 4$	$x 5$	$x 6$	$x 7$	$x 8$	$x 9$	$a 0$	$a 1$	$a 2$	$a 3$	$a 4$	$a 5$	$a 6$	$a 7$	$a 8$
$x 0$	0	0	0	0	1	1	1	1	0	1	1	1	0	1	1	1	1	1	1
$x 1$	0	0	0	1	0	1	0	0	1	0	0	0	1	0	1	0	0	0	0
$x 2$	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0
$x 3$	0	1	1	0	0	0	1	1	0	0	1	0	0	0	0	1	1	0	0
$x 4$	1	0	0	0	0	0	1	1	0	0	1	0	1	1	0	1	0	0	0
$x 5$	1	1	0	0	0	0	1	0	1	1	1	0	0	0	1	0	1	0	1
$x 6$	1	0	0	1	1	1	0	1	1	1	0	0	1	0	0	0	1	1	1
$x 7$	1	0	0	1	1	0	1	0	0	0	0	0	1	1	0	0	1	0	0
$x 8$	0	1	0	0	0	1	1	0	0	1	0	1	0	1	0	1	1	0	0
$x 9$	1	0	0	0	0	1	1	0	1	0	1	0	1	1	0	1	1	0	1

Tab. 3 Rating matrix example

O	$a 0$	$a 1$	$a 2$	$a 3$	$a 4$	$a 5$	$a 6$	$a 7$	$a 8$
$x 0$	5	3	0	5	4	5	1	3	5
$x 1$	0	0	3	0	1	0	0	0	0
$x 2$	0	0	5	0	0	4	0	4	0
$x 3$	2	0	0	0	0	5	4	0	0
$x 4$	5	0	4	4	0	3	0	0	0
$x 5$	3	0	0	0	3	0	4	0	5
$x 6$	0	0	4	0	0	0	4	5	3
$x 7$	0	0	4	5	0	0	3	0	0
$x 8$	0	2	0	5	0	4	3	0	0
$x 9$	4	0	3	2	0	4	4	0	5

Tab. 3 Rating matrix example

O	$a 0$	$a 1$	$a 2$	$a 3$	$a 4$	$a 5$	$a 6$	$a 7$	$a 8$
$x 0$	5	3	0	5	4	5	1	3	5
$x 1$	0	0	3	0	1	0	0	0	0
$x 2$	0	0	5	0	0	4	0	4	0
$x 3$	2	0	0	0	0	5	4	0	0
$x 4$	5	0	4	4	0	3	0	0	0
$x 5$	3	0	0	0	3	0	4	0	5
$x 6$	0	0	4	0	0	0	4	5	3
$x 7$	0	0	4	5	0	0	3	0	0
$x 8$	0	2	0	5	0	4	3	0	0
$x 9$	4	0	3	2	0	4	4	0	5

Tab. 4 Expert community and its user classification under network formal context

$U x$	$x$	$E x$	$L x$
$U 0$	$x 0$	${x 4, x 6, x 9, x 5}$	1
$U 1$	$x 1$	${x 5}$	5
$U 2$	$x 2$	$∅$	4
$U 3$	$x 3$	${x 6}$	4
$U 4$	$x 4$	${x 0, x 6}$	2
$U 5$	$x 5$	${x 0, x 6, x 9}$	2
$U 6$	$x 6$	${x 0, x 4, x 5, x 9}$	3
$U 7$	$x 7$	${x 0, x 4, x 6}$	5
$U 8$	$x 8$	${x 5, x 6, x 9}$	3
$U 9$	$x 9$	${x 0, x 5, x 6}$	1

Tab. 4 Expert community and its user classification under network formal context

$U x$	$x$	$E x$	$L x$
$U 0$	$x 0$	${x 4, x 6, x 9, x 5}$	1
$U 1$	$x 1$	${x 5}$	5
$U 2$	$x 2$	$∅$	4
$U 3$	$x 3$	${x 6}$	4
$U 4$	$x 4$	${x 0, x 6}$	2
$U 5$	$x 5$	${x 0, x 6, x 9}$	2
$U 6$	$x 6$	${x 0, x 4, x 5, x 9}$	3
$U 7$	$x 7$	${x 0, x 4, x 6}$	5
$U 8$	$x 8$	${x 5, x 6, x 9}$	3
$U 9$	$x 9$	${x 0, x 5, x 6}$	1

Tab. 5 Dataset information

数据集	对象数	属性数	稀疏度
Netflix1	2 000	1 200	0.090 3
Netflix2	1 000	1 200	0.092 2
Filmtrust	1 508	2 071	0.011 4
MovieLens-100K	943	1 682	0.063 0
MovieLens-1M	6 040	3 952	0.041 9
MovieLens-10M	3 660	1 100	0.214 1
Jester-s1	500	100	0.400 0
EachMovie-2K	2 000	1 581	0.190 9
EachMovie-3K	3 000	1 648	0.011 7

Fig. 3 Impact of different α on ECBRA recommendation results

Fig. 4 Impact of different θs on ECBRA recommendation results

Fig. 5 Impact of different γ on ECBRA recommendation results

Fig. 6 Comparison of recommendation time between ECBRA and GRAWS

Tab. 6 Comparison of average recommendation times between ECBRA and GRAWS

数据集	ECBRA	GRAWS	数据集	ECBRA	GRAWS
Filmtrust	1	1 306	Netflix2	1	873
MovieLens-100K	1	682	EachMovie-2K	1	578
Netflix1	1	598

Tab. 7 Recommendation performance comparison between ECBRA and GRAWS

数据集	指标	ECBRA	GRAWS
MovieLens-100K	精确度	0.223 9	0.208 7
	召回率	0.285 6	0.120 7
	F1值	0.251 0	0.152 9
Netflix2	精确度	0.226 9	0.120 1
	召回率	0.324 6	0.196 8
	F1值	0.267 1	0.149 1
Filmtrust	精确度	0.445 2	0.232 2
	召回率	0.535 4	0.572 9
	F1值	0.486 2	0.330 4

Tab.8 Recommendation performance comparison of various algorithms

数据集	指标	ECBRA	kNN（k=3）	CSBR	IBCF	GreConD-kNN	CSPR
Netflix1	精确度	0.217 8	0.205 5	0.186 5	0.192 0	0.123 7	0.228 5
	召回率	0.341 2	0.319 6	0.342 3	0.430 2	0.154 9	0.301 9
	F1值	0.265 9	0.250 2	0.241 5	0.265 5	0.137 5	0.260 1
	时间/s	2	11	4	28	8	1
Netflix2	精确度	0.226 9	0.138 7	0.170 7	0.140 5	0.119 3	0.286 2
	召回率	0.324 6	0.201 2	0.180 6	0.271 0	0.155 9	0.198 8
	F1值	0.267 1	0.164 2	0.175 5	0.185 1	0.135 2	0.234 6
	时间/s	1	2	2	8	2	1
Jester-s1	精确度	0.877 2	0.999 7	0.998 5	0.620 6	0.999 8	0.997 8
	召回率	0.959 7	0.774 9	0.607 9	0.696 7	0.809 1	0.814 2
	F1值	0.916 6	0.873 2	0.756 0	0.656 4	0.894 5	0.897 5
	时间/s	1	2	1	2	3	1
MovieLens-10M	精确度	0.295 2	0.241 4	0.230 5	0.187 9	0.153 5	0.349 4
	召回率	0.407 6	0.479 1	0.389 5	0.593 1	0.241 8	0.300 3
	F1值	0.342 4	0.321 1	0.289 6	0.285 4	0.187 8	0.323 0
	时间/s	5	57	18	65	40	6
MovieLens-100K	精确度	0.223 9	0.201 5	0.244 4	0.192 4	0.116 6	0.278 1
	召回率	0.285 6	0.345 0	0.217 7	0.403 3	0.173 2	0.215 8
	F1值	0.251 0	0.254 4	0.230 3	0.260 5	0.139 4	0.243 0
	时间/s	1	1	1	15	2	1
MovieLens-1M	精确度	0.182 3	0.188 2	0.270 0	0.152 7	0.111 7	0.207 3
	召回率	0.271 0	0.340 4	0.152 4	0.391 8	0.147 5	0.276 3
	F1值	0.217 9	0.242 4	0.194 8	0.219 7	0.127 1	0.236 9
	时间/s	15	337	64	1 168	163	12
Filmtrust	精确度	0.445 2	0.465 6	0.452 9	0.127 5	0.452 5	0.446 6
	召回率	0.535 4	0.464 4	0.484 2	0.231 4	0.413 7	0.626 6
	F1值	0.486 2	0.465 0	0.468 0	0.164 4	0.432 2	0.521 5
	时间/s	6	5	1	52	3	1
EachMovie-2K	精确度	0.192 6	0.198 8	0.159 7	0.084 6	0.142 3	0.154 0
	召回率	0.267 3	0.296 3	0.362 4	0.127 9	0.164 0	0.361 0
	F1值	0.223 8	0.238 0	0.221 6	0.101 8	0.152 4	0.215 9
	时间/s	1	6	1	46	2	2
EachMovie-3K	精确度	0.192 2	0.209 2	0.174 8	0.185 4	0.150 6	0.221 0
	召回率	0.237 7	0.280 5	0.331 0	0.126 3	0.155 5	0.242 2
	F1值	0.212 5	0.239 6	0.228 7	0.150 3	0.153 0	0.231 1
	时间/s	1	18	2	71	8	2

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Construction and recommendation application of expert communities based on user-centric approach

基于用户中心的专家社区构建及推荐应用

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