基于主观逻辑方法的消费者多源信任融合模型
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摘要
社会化商务中,消费者依赖在线口口相传建立感知信任,其本质是复杂网络上的多源信任融合问题。国内外学者对多源信任融合问题进行了大量研究,并以主观逻辑方法为代表形成了信任融合方法的研究体系。然而,由于社交网络中消费者感知信任的多源性和高度主观性以及用户生成内容的海量化,给多源信任融合模型带来量化难、实时处理难和融合难等问题。针对上述难题,提出"先聚类、后融合"的研究思路,先对海量推荐信息进行聚类,再融入感知信任的主观因素构建多源信任融合模型。首先,将推荐信息间的相似性作为节点关系,从社交网络中抽取出推荐信息相似性网络,用谱平分方法对聚簇进行划分,实现对推荐信息的聚类;其次,用网络属性度量感知信任的影响因素,从复杂网络视角出发,提出消费者感知信任定性因素的量化方法;再次,以多属性决策方法为基础,改进主观逻辑方法构建多源信任融合模型,从而将感知信任的影响因素融入主观逻辑方法,突破主观逻辑方法只考虑推荐信息和网络路径的局限性;最后,通过仿真实验对推荐信息实验数据进行聚类,并分析主观因素对感知信任意见空间的调节作用,验证模型的可行性。研究结果表明,研究模型能够快速划分推荐信息相似性网络,客观地量化感知信任的影响因素,使其融入信任度计算之中,且能够体现消费者感知信任的主观性和异质性。从仿真实验结果看,该模型能够有效解决大规模社会网络中推荐信息海量化问题,权威程度、从众行为和主体间亲密度等影响因素对信任度计算结果起调节作用。该模型将信任融合模型扩展到社会化商务领域,可以帮助商家评价已有消费群体对新消费者感知信任的影响力,为大规模网络中消费者感知信任的度量和预测提供新视角,为商家实时分析消费者感知信任意向和制定营销策略提供方法支持。对于难以建立新消费者信任的商家,可以通过制定激励机制来提高用户生成内容量,培养或引入权威人士作为明星节点,建立有主题的小社区等形式来促进主体间交流,增强消费者之间的亲密度,从而提高消费者感知信任。
In social commerce,consumers establish perceived trust through online word-of-mouth( WOM),whose essence is multi-source trust fusion which has been fully analyzed by scholars worldwide and an analysis system has been formed based on subjective logic. However,due to multi-sources and high subjectivity of consumers' perceived trust as well as massive user-generated content( UGC),there still exist some key issues in trust fusion model development in relation to quantitative processing,real-time processing and information fusion given the context of social commerce.We propose a method of "clustering before fusion"to establish the multi-source trust fusion model. First,this study takes similarity of recommendation as relationships between nodes to extract recommendation similarity network from social networks,and then partitions the recommendation similarity network with spectral bisection in order to cluster recommendation information.Second,we come up with the quantitative approach of perceived trust from the perspective of complex network. Third,we establish a multi-source trust fusion model by improving subjective logic based on multiple attribute decision making. The model integrates influential factors and breaks through the limits that subjective logic only takes into account the recommendations and network path. Finally,we verify the feasibility of the model. The data of recommendation experiments are clustered by simulation experiments and the mediating effects of subjective factors on perceived trust space are tested.This study indicates that the recommendation can be modular in similarity network rapidly with the use of our model mentioned above. In addition,measuring the influential factors objectively and taking account into trust degree calculation will deliver the subjectivity and heterogeneity of consumers. Based on the simulation results,massive recommendation information in social networks has been effectively addressed by our model. Meanwhile,the influential factors such as authority,herd behavior and closeness act a mediating effect in the calculation result of trust degree.Extending the use of trust fusion model into social commerce can evaluate the spreading effect from existing consumers to new consumers in perceived trust. Thus this model provides a new perspective for measuring and forecasting consumers' perceived trust in massive social networks,and offers sellers a method in real-time analysis of consumers' perceived trust intention and market strategy development. For the sellers with obstacles in new consumers' trust establishing,raising the quantity of UGC will be a good choice. Developing stimulating mechanism,fostering or introducing authority as stars,building small communities to promote communication can be alternatives to finally enhance the closeness between consumers.
In social commerce,consumers establish perceived trust through online word-of-mouth( WOM),whose essence is multi-source trust fusion which has been fully analyzed by scholars worldwide and an analysis system has been formed based on subjective logic. However,due to multi-sources and high subjectivity of consumers' perceived trust as well as massive user-generated content( UGC),there still exist some key issues in trust fusion model development in relation to quantitative processing,real-time processing and information fusion given the context of social commerce.We propose a method of "clustering before fusion"to establish the multi-source trust fusion model. First,this study takes similarity of recommendation as relationships between nodes to extract recommendation similarity network from social networks,and then partitions the recommendation similarity network with spectral bisection in order to cluster recommendation information.Second,we come up with the quantitative approach of perceived trust from the perspective of complex network. Third,we establish a multi-source trust fusion model by improving subjective logic based on multiple attribute decision making. The model integrates influential factors and breaks through the limits that subjective logic only takes into account the recommendations and network path. Finally,we verify the feasibility of the model. The data of recommendation experiments are clustered by simulation experiments and the mediating effects of subjective factors on perceived trust space are tested.This study indicates that the recommendation can be modular in similarity network rapidly with the use of our model mentioned above. In addition,measuring the influential factors objectively and taking account into trust degree calculation will deliver the subjectivity and heterogeneity of consumers. Based on the simulation results,massive recommendation information in social networks has been effectively addressed by our model. Meanwhile,the influential factors such as authority,herd behavior and closeness act a mediating effect in the calculation result of trust degree.Extending the use of trust fusion model into social commerce can evaluate the spreading effect from existing consumers to new consumers in perceived trust. Thus this model provides a new perspective for measuring and forecasting consumers' perceived trust in massive social networks,and offers sellers a method in real-time analysis of consumers' perceived trust intention and market strategy development. For the sellers with obstacles in new consumers' trust establishing,raising the quantity of UGC will be a good choice. Developing stimulating mechanism,fostering or introducing authority as stars,building small communities to promote communication can be alternatives to finally enhance the closeness between consumers.
引文
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[6]J?SANG A.The consensus operator for combining beliefs.Artificial Intelligence,2002,141(1/2):157-170.
[7]YAN S R,ZHENG X L,WANG Y,et al.A graph-based comprehensive reputation model:exploiting the social context of opinions to enhance trust in social commerce.Information Sciences,2015,318:51-72.
[8]ZHOU L,ZHANG P,ZIMMERMANN H D.Social commerce research:an integrated view.Electronic Commerce Research and Applications,2013,12(2):61-68.
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[11]CHEN J,SHEN X L.Consumers'decisions in social commerce context:an empirical investigation.Decision Support Systems,2015,79:55-64.
[12]HAJLI M N.A study of the impact of social media on consumers.International Journal of Market Research,2014,56(3):387-404.
[13]WEISBERG J,TE'ENI D,ARMAN L.Past purchase and intention to purchase in e-commerce:the mediation of social presence and trust.Internet Research,2011,21(1):82-96.
[14]刘志明,刘鲁.微博网络舆情中的意见领袖识别及分析.系统工程,2011,29(6):8-16.LIU Zhiming,LIU Lu.Recognition and analysis of opinion leaders in microblog public opinions.Systems Engineering,2011,29(6):8-16.(in Chinese)
[15]SUMMERS J O.The identity of women's clothing fashion opinion leaders.Journal of Marketing Research,1970,7(2):178-185.
[16]BANSAL H S,VOYER P A.Word-of-mouth processes within a services purchase decision context.Journal of Service Research,2000,3(2):166-177.
[17]FRIEDMAN H H,FRIEDMAN L.Endorser effectiveness by product type.Journal of Advertising Research,1979,19(5):63-71.
[18]梦非.社会化商务环境下意见领袖对购买意愿的影响研究.南京:南京大学,2012:58-59.MENG Fei.Research of opinion leaders'impact on purchase intention under social commerce context.Nanjing:Nanjing University,2012:58-59.(in Chinese)
[19]LUO B,LIN Z.A decision tree model for herd behavior and empirical evidence from the online P2P lending market.Information Systems and e-Business Management,2013,11(1):141-160.
[20]LIU Y,SUTANTO J.Buyers'purchasing time and herd behavior on deal-of-the-day group-buying websites.Electronic Markets,2012,22(2):83-93.
[21]CHEN Y F.Herd behavior in purchasing books online.Computers in Human Behavior,2008,24(5):1977-1992.
[22]HAJLI M.Social commerce adoption model∥UK Academy for Information Systems Conference Proceedings 2012.Oxford,2012:1-26.
[23]SEE-TO E W K,HO K K W.Value co-creation and purchase intention in social network sites:the role of electronic word-of-mouth and trust:a theoretical analysis.Computers in Human Behavior,2014,31:182-189.
[24]HAJLI M,HAJLI M,KHANI F.Establishing trust in social commerce through social word of mouth.International Journal of Information Science and Management,2013(Special issue):39-53.
[25]CHEN J V,SU B,WIDJAJA A E.Facebook C2C social commerce:a study of online impulse buying.Decision Support Systems,2016,83:57-69.
[26]WANG Y,YU C.Social interaction-based consumer decision-making model in social commerce:the role of word of mouth and observational learning.Auburn,Alabama:Auburn University,2015.
[27]SHIN D H.User experience in social commerce:in friends we trust.Behaviour&Information Technology,2013,32(11):1191-1192.
[28]BERGER J.Word of mouth and interpersonal communication:a review and directions for future research.Journal of Consumer Psychology,2014,24(4):586-607.
[29]YAN S R,ZHENG X L,WANG Y,et al.A graph-based comprehensive reputation model:exploiting the social context of opinions to enhance trust in social commerce.Information Sciences,2015,318:51-72.
[30]GRANOVETTER M,SOONG R.Threshold models of interpersonal effects in consumer demand.Journal of Economic Behavior&Organization,1986,7(1):83-99.
[31]鄢章华,滕春贤,刘蕾.供应链信任传递机制及其均衡研究.管理科学,2010,23(6):64-71.YAN Zhanghua,TENG Chunxian,LIU Lei.Transfer mechanism of supply chain trust and its equilibrium.Journal of Management Science,2010,23(6):64-71.(in Chinese)
[32]SHAFER G.A mathematical theory of evidence.Princeton,NJ:Princeton University Press,1976:28-35.
[33]SHAFER G.Dempster's rule of combination.International Journal of Approximate Reasoning,2016,79:26-40.
[34]田博,覃正.B2C电子商务中基于D-S证据融合理论的推荐信任评价模型.管理科学,2008,21(5):98-104.TIAN Bo,QIN Zheng.Recommended trust evaluation model in business-to-consumer e-commerce based on D-S evidence fusion theory.Journal of Management Science,2008,21(5):98-104.(in Chinese)
[35]成坚,冯仁剑,许小丰,等.基于D-S证据理论的无线传感器网络信任评估模型.传感技术学报,2009,22(12):1802-1807.CHENG Jian,FENG Renjian,XU Xiaofeng,et al.Trust evaluation model based on D-S evidence theory in wireless sensor networks.Chinese Journal of Sensors and Actuators,2009,22(12):1802-1807.(in Chinese)
[36]梁昌勇,陈增明,黄永青,等.Dempster-Shafer合成法则悖论的一种消除方法.系统工程理论与实践,2005,25(3):7-12,85.LIANG Changyong,CHEN Zengming,HUANG Yongqing,et al.A method of dispelling the absurdities of Dempster-Shafer's rule of combination.Systems Engineering-Theory&Practice,2005,25(3):7-12,85.(in Chinese)
[37]J?SANG A.The consensus operator for combining beliefs.Artificial Intelligence,2002,141(1/2):157-170.
[38]J?SANG A.A logic for uncertain probabilities.International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems,2001,9(3):279-311.
[39]KAPLAN L,SENSOY M,CHAKRABORTY S.Partial observable update for subjective logic and its application for trust estimation.Information Fusion,2015,26:66-83.
[40]焦洪强.基于拓展主观逻辑的电子商务信任问题研究.保定:河北大学,2015:24-40.JIAO Hongqiang.Research of e-commerce trust problem based on expanded subjective logic.Baoding:Hebei University,2015:24-40.(in Chinese)
[41]王进,孙怀江.基于J6sang信任模型的信任传递与聚合研究.控制与决策,2009,24(12):1885-1889.WANG Jin,SUN Huaijiang.Trust transitivity and aggregation research based on J6sang's trust model.Control and Decision,2009,24(12):1885-1889.(in Chinese)
[42]NG C S P.Intention to purchase on social commerce websites across cultures:a cross-regional study.Information&Management,2013,50(8):609-620.
[43]CAPOCCI A,SERVEDIO V D P,CALDARELLI G,et al.Detecting communities in large networks.Physica A:Statistical Mechanics and Its Applications,2005,352(2/4):669-676.
[44]刘军.社会网络分析导论.北京:社会科学文献出版社,2004:116-117.LIU Jun.An introduction to social network analysis.Beijing:Social Sciences Academic Press(China),2004:116-117.(in Chinese)
[2]STEPHEN A T,TOUBIA O.Deriving value from social commerce networks.Journal of Marketing Research,2010,47(2):215-228.
[3]中国电子商务研究中心.2015年第一季度微店报告.(2015-05-28)[2016-01-23].http:∥b2b.toocle.com/detail-6253698.html.China e-Commerce Research Center(CECRC).Micro shop report for the first quarter of 2015.(2015-05-28)[2016-01-23].http:∥b2b.toocle.com/detail-6253698.html.(in Chinese)
[4]李建标,李朝阳.信任的信念基础:实验经济学的检验.管理科学,2013,26(2):62-71.LI Jianbiao,LI Chaoyang.An experimental economics test on belief as the basis of trust.Journal of Management Science,2013,26(2):62-71.(in Chinese)
[5]徐彪,张媛媛,张珣.负面事件后消费者信任受损及其外溢机理研究.管理科学,2014,27(2):95-107.XU Biao,ZHANG Yuanyuan,ZHANG Xun.A study on mechanism of consumer trust damage and its spillover after negative events.Journal of Management Science,2014,27(2):95-107.(in Chinese)
[6]J?SANG A.The consensus operator for combining beliefs.Artificial Intelligence,2002,141(1/2):157-170.
[7]YAN S R,ZHENG X L,WANG Y,et al.A graph-based comprehensive reputation model:exploiting the social context of opinions to enhance trust in social commerce.Information Sciences,2015,318:51-72.
[8]ZHOU L,ZHANG P,ZIMMERMANN H D.Social commerce research:an integrated view.Electronic Commerce Research and Applications,2013,12(2):61-68.
[9]BROWN J J,REINGEN P H.Social ties and word-of-mouth referral behavior.Journal of Consumer Research,1987,14(3):350-362.
[10]KIM S,PARK H.Effects of various characteristics of social commerce(s-commerce)on consumers'trust and trust performance.International Journal of Information Management,2013,33(2):318-332.
[11]CHEN J,SHEN X L.Consumers'decisions in social commerce context:an empirical investigation.Decision Support Systems,2015,79:55-64.
[12]HAJLI M N.A study of the impact of social media on consumers.International Journal of Market Research,2014,56(3):387-404.
[13]WEISBERG J,TE'ENI D,ARMAN L.Past purchase and intention to purchase in e-commerce:the mediation of social presence and trust.Internet Research,2011,21(1):82-96.
[14]刘志明,刘鲁.微博网络舆情中的意见领袖识别及分析.系统工程,2011,29(6):8-16.LIU Zhiming,LIU Lu.Recognition and analysis of opinion leaders in microblog public opinions.Systems Engineering,2011,29(6):8-16.(in Chinese)
[15]SUMMERS J O.The identity of women's clothing fashion opinion leaders.Journal of Marketing Research,1970,7(2):178-185.
[16]BANSAL H S,VOYER P A.Word-of-mouth processes within a services purchase decision context.Journal of Service Research,2000,3(2):166-177.
[17]FRIEDMAN H H,FRIEDMAN L.Endorser effectiveness by product type.Journal of Advertising Research,1979,19(5):63-71.
[18]梦非.社会化商务环境下意见领袖对购买意愿的影响研究.南京:南京大学,2012:58-59.MENG Fei.Research of opinion leaders'impact on purchase intention under social commerce context.Nanjing:Nanjing University,2012:58-59.(in Chinese)
[19]LUO B,LIN Z.A decision tree model for herd behavior and empirical evidence from the online P2P lending market.Information Systems and e-Business Management,2013,11(1):141-160.
[20]LIU Y,SUTANTO J.Buyers'purchasing time and herd behavior on deal-of-the-day group-buying websites.Electronic Markets,2012,22(2):83-93.
[21]CHEN Y F.Herd behavior in purchasing books online.Computers in Human Behavior,2008,24(5):1977-1992.
[22]HAJLI M.Social commerce adoption model∥UK Academy for Information Systems Conference Proceedings 2012.Oxford,2012:1-26.
[23]SEE-TO E W K,HO K K W.Value co-creation and purchase intention in social network sites:the role of electronic word-of-mouth and trust:a theoretical analysis.Computers in Human Behavior,2014,31:182-189.
[24]HAJLI M,HAJLI M,KHANI F.Establishing trust in social commerce through social word of mouth.International Journal of Information Science and Management,2013(Special issue):39-53.
[25]CHEN J V,SU B,WIDJAJA A E.Facebook C2C social commerce:a study of online impulse buying.Decision Support Systems,2016,83:57-69.
[26]WANG Y,YU C.Social interaction-based consumer decision-making model in social commerce:the role of word of mouth and observational learning.Auburn,Alabama:Auburn University,2015.
[27]SHIN D H.User experience in social commerce:in friends we trust.Behaviour&Information Technology,2013,32(11):1191-1192.
[28]BERGER J.Word of mouth and interpersonal communication:a review and directions for future research.Journal of Consumer Psychology,2014,24(4):586-607.
[29]YAN S R,ZHENG X L,WANG Y,et al.A graph-based comprehensive reputation model:exploiting the social context of opinions to enhance trust in social commerce.Information Sciences,2015,318:51-72.
[30]GRANOVETTER M,SOONG R.Threshold models of interpersonal effects in consumer demand.Journal of Economic Behavior&Organization,1986,7(1):83-99.
[31]鄢章华,滕春贤,刘蕾.供应链信任传递机制及其均衡研究.管理科学,2010,23(6):64-71.YAN Zhanghua,TENG Chunxian,LIU Lei.Transfer mechanism of supply chain trust and its equilibrium.Journal of Management Science,2010,23(6):64-71.(in Chinese)
[32]SHAFER G.A mathematical theory of evidence.Princeton,NJ:Princeton University Press,1976:28-35.
[33]SHAFER G.Dempster's rule of combination.International Journal of Approximate Reasoning,2016,79:26-40.
[34]田博,覃正.B2C电子商务中基于D-S证据融合理论的推荐信任评价模型.管理科学,2008,21(5):98-104.TIAN Bo,QIN Zheng.Recommended trust evaluation model in business-to-consumer e-commerce based on D-S evidence fusion theory.Journal of Management Science,2008,21(5):98-104.(in Chinese)
[35]成坚,冯仁剑,许小丰,等.基于D-S证据理论的无线传感器网络信任评估模型.传感技术学报,2009,22(12):1802-1807.CHENG Jian,FENG Renjian,XU Xiaofeng,et al.Trust evaluation model based on D-S evidence theory in wireless sensor networks.Chinese Journal of Sensors and Actuators,2009,22(12):1802-1807.(in Chinese)
[36]梁昌勇,陈增明,黄永青,等.Dempster-Shafer合成法则悖论的一种消除方法.系统工程理论与实践,2005,25(3):7-12,85.LIANG Changyong,CHEN Zengming,HUANG Yongqing,et al.A method of dispelling the absurdities of Dempster-Shafer's rule of combination.Systems Engineering-Theory&Practice,2005,25(3):7-12,85.(in Chinese)
[37]J?SANG A.The consensus operator for combining beliefs.Artificial Intelligence,2002,141(1/2):157-170.
[38]J?SANG A.A logic for uncertain probabilities.International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems,2001,9(3):279-311.
[39]KAPLAN L,SENSOY M,CHAKRABORTY S.Partial observable update for subjective logic and its application for trust estimation.Information Fusion,2015,26:66-83.
[40]焦洪强.基于拓展主观逻辑的电子商务信任问题研究.保定:河北大学,2015:24-40.JIAO Hongqiang.Research of e-commerce trust problem based on expanded subjective logic.Baoding:Hebei University,2015:24-40.(in Chinese)
[41]王进,孙怀江.基于J6sang信任模型的信任传递与聚合研究.控制与决策,2009,24(12):1885-1889.WANG Jin,SUN Huaijiang.Trust transitivity and aggregation research based on J6sang's trust model.Control and Decision,2009,24(12):1885-1889.(in Chinese)
[42]NG C S P.Intention to purchase on social commerce websites across cultures:a cross-regional study.Information&Management,2013,50(8):609-620.
[43]CAPOCCI A,SERVEDIO V D P,CALDARELLI G,et al.Detecting communities in large networks.Physica A:Statistical Mechanics and Its Applications,2005,352(2/4):669-676.
[44]刘军.社会网络分析导论.北京:社会科学文献出版社,2004:116-117.LIU Jun.An introduction to social network analysis.Beijing:Social Sciences Academic Press(China),2004:116-117.(in Chinese)