基于用户动态交互行为扩展的信念网络推荐模型  

Extended belief network recommendation model based on user dynamic interaction behavior

作　　者：鲍彩倩 徐建民 张国防 BAO Caiqian;XU Jianmin;ZHANG Guofang(School of Cyber Security and Computer,Hebei University,Baoding Hebei 071002,China;School of Management,Hebei University,Baoding Hebei 071002,China)

机构地区：[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

出　　处：《计算机应用》2023年第4期1115-1121,共7页journal of Computer Applications

基　　金：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)。

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。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.

关 键 词：信念网络 动态交互 推荐模型 社交媒体 合取 析取 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]