Extended belief network recommendation model based on user dynamic interaction behavior

doi:10.11772/j.issn.1001-9081.2022020279

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (4): 1115-1121.DOI: 10.11772/j.issn.1001-9081.2022020279

• Data science and technology • Previous Articles

Extended belief network recommendation model based on user dynamic interaction behavior

Caiqian BAO¹, Jianmin XU¹(), Guofang ZHANG²

^1.School of Cyber Security and Computer，Hebei University，Baoding Hebei 071002，China
^2.School of Management，Hebei University，Baoding Hebei 071002，China

Received:2022-03-11 Revised:2022-05-24 Accepted:2022-05-26 Online:2022-08-16 Published:2023-04-10
Contact: Jianmin XU
About author:BAO Caiqian， born in 1999， M. S. candidate. Her research interests include online social network.
ZHANG Guofang， born in 1979， Ph. D.， lecturer. His research interests include information retrieval， online social network.
Supported by:
Latter Project of National Social Science Foundation of China(17FTQ002);Natural Science Foundation of Hebei Province(F2015201142)

基于用户动态交互行为扩展的信念网络推荐模型

鲍彩倩¹, 徐建民¹(), 张国防²

^1.河北大学网络空间安全与计算机学院，河北保定 071002
^2.河北大学管理学院，河北保定 071002

通讯作者: 徐建民
作者简介:鲍彩倩（1999—），女，河北石家庄人，硕士研究生，CCF会员，主要研究方向：在线社交网络；
张国防（1979—），男，河北保定人，讲师，博士，主要研究方向：信息检索、在线社交网络。
基金资助:
国家社会科学基金后期资助项目(17FTQ002);河北省自然科学基金资助项目(F2015201142)

Abstract

Abstract:

An Extended Belief Network Recommendation model based on User Dynamic Interaction Behavior （EBNR_UDIB） was proposed to solve the problem of failing to consider accuracy and diversity simultaneously in current recommendation methods due to the unitary way to combine evidence. Firstly， a three-layer basic Belief Network Recommendation （BNR） model was constructed to provide an effective and flexible framework for the introduction of evidence. Secondly， by analyzing direct and coupled interaction relationships among users， the interaction strength was calculated， and this strength was further adjusted by a dynamic time decay factor. Finally， taking the interest of user weighted by this strength as new evidence， EBNR_UDIB was obtained by using two combination ways of evidence： conjunction and disjunction. Experimental results show that compared with Content-Based Recommendation Model （CBRM） and Social relationship-Based Recommendation Model （SBRM）， the proposed model has the accuracy， recall， and F1-measure increased by at least 7， 4， and 5 percentage points respectively under conjunction combination way， and increased by least 2， 8， and 6 percentage points respectively under disjunction combination way； on the diversity and novelty metrics， the proposed model under disjunction combination way is improved by least 15 and 6 percentage points respectively compared to the above two models， and the proposed model under conjunction combination way outperforms the comparison models at the same time.

Key words: belief network, dynamic interaction, recommendation model, social media, conjunction, disjunction

摘要：

针对现有推荐方法证据组合方式单一，未能同时考虑准确性和多样性的问题，提出基于用户动态交互行为扩展的信念网络推荐模型（EBNR_UDIB）。首先，构建一个具有3层结构的基本信念网络推荐（BNR）模型，从而为证据的引入提供一个灵活有效的框架；其次，通过分析用户间的直接及耦合交互关系计算交互强度，并引入动态调整的时间衰减因子修订该强度；最后，以该强度对交互用户加权，将该用户的兴趣作为新证据扩展基本模型，并利用合取和析取两种证据组合方式得到EBNR_UDIB。实验结果表明，相较于基于内容的推荐模型（CBRM）和基于社交的推荐模型（SBRM），在准确率、召回率和F1值上，合取组合方式下的所提模型分别至少提升了7、4和5个百分点，析取组合方式下的所提模型分别至少提升了2、8和6个百分点；在多样性和新颖性指标上，析取组合方式下的所提模型分别至少提升了15和6个百分点，合取组合方式下的所提模型也优于对比模型。

关键词: 信念网络, 动态交互, 推荐模型, 社交媒体, 合取, 析取

CLC Number:

TP391

Caiqian BAO, Jianmin XU, Guofang ZHANG. Extended belief network recommendation model based on user dynamic interaction behavior[J]. Journal of Computer Applications, 2023, 43(4): 1115-1121.

鲍彩倩, 徐建民, 张国防. 基于用户动态交互行为扩展的信念网络推荐模型[J]. 《计算机应用》唯一官方网站, 2023, 43(4): 1115-1121.

Figures/Tables 9

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用户	静态交互强度	动态交互强度
ID：1684838781	0.287 4	0.269 1
ID：1964364687	0.186 9	0.127 6
ID：1942778191	0.159 6	0.152 3
ID：2011820832	0.105 4	0.134 6
ID：2123070243	0.018 5	0.007 4

用户	静态交互强度	动态交互强度
ID：1684838781	0.287 4	0.269 1
ID：1964364687	0.186 9	0.127 6
ID：1942778191	0.159 6	0.152 3
ID：2011820832	0.105 4	0.134 6
ID：2123070243	0.018 5	0.007 4

信息交互行为	信息交互行为
信息交互行为	转发	评论	点赞
转发	1	2	3
评论	1/2	1	2
点赞	1/3	1/2	1

信息交互行为	信息交互行为
信息交互行为	转发	评论	点赞
转发	1	2	3
评论	1/2	1	2
点赞	1/3	1/2	1

模型	准确率	召回率	F1值	MRR
BNR	0.63	0.47	0.54	0.42
EBNR_UDIB-AND	0.80	0.54	0.64	0.44
EBNR_UDIB-OR	0.75	0.58	0.65	0.43

Extended belief network recommendation model based on user dynamic interaction behavior

基于用户动态交互行为扩展的信念网络推荐模型

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References 24

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模型	准确率	召回率	F1值	多样性	新颖性
EBNR_UDIB-AND	0.80	0.54	0.64	0.80	0.91
EBNR_UDIB-OR	0.75	0.58	0.65	0.95	0.96
SBRM	0.73	0.50	0.59	0.80	0.90
CBRM	0.69	0.43	0.53	0.74	0.86

模型	准确率	召回率	F1值	多样性	新颖性
EBNR_UDIB-AND	0.80	0.54	0.64	0.80	0.91
EBNR_UDIB-OR	0.75	0.58	0.65	0.95	0.96
SBRM	0.73	0.50	0.59	0.80	0.90
CBRM	0.69	0.43	0.53	0.74	0.86

模型	运行时间
CBRM	1 938
SBRM	1 988
EBNR_UDIB-AND	1 724
EBNR_UDIB-OR	2 045

模型	运行时间
CBRM	1 938
SBRM	1 988
EBNR_UDIB-AND	1 724
EBNR_UDIB-OR	2 045