面向服务的消费者行为分析及推荐模型研究
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摘要
随着互联网应用在商业领域的快速普及，消费者的需求体验成为驱动互联网发展的领航者。与此同时，以电子商务为代表的社交网络不断发生新的变化。在计算机系统和社会交互系统高度融合的电子商务中，消费者承担着越来越重要的作用，消费者的行为特征以及潜在的需求逐渐成为技术发展的重要推动力量。因此，研究消费者决策行为、分析消费者的信息特征、挖掘消费者行为中潜在的规律及消费者之间的关联度，能够指导计算机网络技术更好的发展，使电子商务向智能化和人性化发展，从而与现实社会系统更加和谐的融合。
在线消费者行为的研究综合了社会心理学、计算机科学、经济学、营销学、行为科学、物理学以及复杂网络等学科，研究的目标是指导计算机网络技术特别是电子商务更好的为社会发展服务。大量企业的协同过滤推荐系统通过建立消费者预测模型，提供个性化推荐服务，解决信息过载等问题。本文基于上述需求，深入研究了服务理论研究进展和服务质量管理理论，对服务科学的研究起源、研究现状、研究方法进行了全面分析，并深刻挖掘了服务科学研究目前存在的问题，运用现代营销学中的服务质量评价和满意度理论，构建了服务主导逻辑的理论研究框架，为企业提高服务创新能力提供实践指导，并为系统规划提供了理论方向，主要内容包括以下四点：
1、提出消费者购买决策行为研究模型
基于初始信任和TAM理论，通过访谈和问卷调查，对消费者基于在线电子供应商的首次购买行为进行实证研究。为增强消费者对电子供应商的首次购买意图，加入一个新的变量，即对电子供应商的感知服务质量，它直接影响首次购买意图，根据分析结果提出了一个垂直的理论体系。
在这个理论体系下，初始信任的建立和感知服务质量理论对新的在线消费者首次购买意图非常关键，由数据分析结果发现了各种影响因素对消费者意图行为的作用，提醒企业在规划网站在线购物系统各构成要素时要重视对于初始信任的建立，而消费者的感知服务质量也不容忽视。
2、分析电子商务中长尾理论并将其应用到电影推荐中
长尾理论认为随着信息技术的发展，经济发展的趋势是从纯原子经济学到字节和原子的混合然后成为纯字节的经济，通过对大数据中潜在信息的分析可以推荐出用户真正满意的产品和服务。
不存在能够适用于所有产品的推荐系统，只有用户深度参与的个性化推荐才能够根据碎片化的信息在一定的准确度和一定的多样性上猜测消费者的偏好，并据此向用户推荐，这些个性化推荐技术不仅能节省用户浏览搜索的时间，并且能找到一些网络角落里的暗信息，挖掘到信息海洋中的长尾利润。本文在深入总结了长尾理论相关问题的基础上，分析了长尾理论在电影推荐中的应用。
3、提出并验证了基于多样性的协同过滤推荐模型
传统的在线推荐系统的准确性多依赖于协同过滤推荐算法，然而推荐系统的目的是吸引消费者的兴趣，将浏览者转变为购买者，而不是准确的预测他们的评分。
在线推荐系统是社会过滤过程的服务器版本，前期的研究大多强调协同过滤算法的精确性。而有效的推荐系统必须是可信任的，而且要求系统逻辑透明度好，能够提供给消费者新的、无经验的项目。本文基于此提出从用户体验的视角研究推荐系统的质量评价，在推荐候选集合算法中增加了一个新鲜度因子，利用协同过滤相似度计算方法结合聚簇算法进行个性化推荐，推荐结果与传统的推荐算法进行比较，实验结果具有一定的准确度和较高的多样性，从而为更好的建立电子商务推荐系统提供了依据。
4、提出基于最大熵的情感文本识别算法
由于互联网上海量的消费者文本评论信息对其他消费者感知企业的口碑信誉有重要的影响，而且蕴藏了大量消费者的偏好和行为特征，通过文本评论信息分析技术，企业可以了解消费者对企业信誉、产品或服务的感知服务质量和个性化偏好，从而有针对性地制定企业战略，改进信息系统体系结构，提供个性化推荐项目，提高竞争实力。
本文提出了一种基于最大熵模型的在线评论情感搭配识别算法，用来识别网络文本评论的情感倾向。设计了基于情感词语语义特征的最大熵模型算法，根据同义词词表将情感文本评论语料中的同义词或近义词词语作为一个语义特征类，选择包含某一语义特征类的情感文本评论语料组成基于最大熵的原子特征模板和复合特征模板，进行自动的识别。对文本情感评论语料中评价词和评价对象之间搭配关系的判断属于一个搭配或非搭配的二分类问题。本文基于最大熵模型对评价搭配进行二分类，并通过构建极性词表以扩充最大熵特征模板，实验证明该模型能够提高分类的准确率。
With the rapid popularization of Internet application in commercial field, the consumer’sdemand becomes the pilot which drives the development of Internet. Meanwhile, the socialnetwork represented by e-commerce varies constantly. In the e-commerce which is highlyintegrated of computer system and social interaction system, the consumers become more andmore important; the consumers’ behavior characteristics and their potential needs graduallybecome the important power to impel the development of technology. Therefore, to researchconsumers decision behavior, to analyze consumers information characteristics, as well as todiscover the underlying rules in consumer behavior and the relational degree between consumers,could guide the computer network develop better and make e-commerce develop towards thedirection of more intelligent and more human, thus mix together with the real social system moreharmoniously.
The research on online consumer behavior closely links with many subjects, such as, socialpsychology, computer science, economics, marketing, anthropology, physics and everything todo with complex network. The ultimate goal is to guide the computer network technology,especially e-commerce, providing a better service to the development of human society. A largenumber of enterprises’ collaborative filtering recommendation systems solve such problems asinformation overload, through establishing consumer prediction model and providingpersonalized service. Based on the above requirements, this paper goes further into the researchprogress of service theory and the service quality management theory, analyzingcomprehensively the research origin, research status and research method of service science,excavating profoundly the existing problems of the study on service science. With the servicequality evaluation and satisfaction theory in modern marketing, this research builds thetheoretical research framework serving the dominant logic, providing practical guidance forenterprises to improve the service innovation ability and theoretical direction for systemplanning. The research content includes the following aspects.
1. Presenting the research model on consumers buying decision behavior
Based on the initial trust and TAM theory, this paper gives the empirical research on theconsumers first time buying behavior based on the online electronic supplier through interviewsand questionnaire survey. In order to enhance consumer’s intention of the first time buying of theelectronic supplier, the research adds a new variable, namely the perceived service quality for theelectronic suppliers which influence directly the first time purchase intention. According to theresults of the analysis, the research puts forward a vertical theory system.
In this theory system, the establishment of initial trust and perceived service quality theoryare critical for the new online consumer’s intention of the first time purchase. According to theresult of the statistical analysis, this research finds the effect of various kinds of influence factorson consumers’ intention behavior, reminding the enterprises to pay attention to the establishmentof initial trust in projecting the websites elements of online shopping system, while consumersperceived service quality cannot be neglected.
2. Analyzing the long tail theory in electronic commerce and applying it to the movierecommendation
In the long tail theory, with the development of information technology, the trend ofeconomic development is from a mixture of pure economics to byte and atoms, then to theeconomy of pure bytes. It could recommend the really satisfying products and services to theusers through the analysis of the potential information in big data.
There is no recommendation system which could be applied to all products. Only the userswho are deeply involved in the personalized recommendation, can the system speculates theirpreferences in a certain accuracy and diversity according to the fragments of information, andrecommends to the user according to this. The personalized recommendation technology can notonly save users browse search time, but also can find some potential information in the corner ofthe network, digging out the profit from the long tail theory in the information ocean. On thebasis of deep summary of related issues of the long tail theory, this paper analyzes the long tailtheory application in movie recommendations.
3. Proposing and verifying the collaborative filtering recommendation model based ondiversity
The accuracy of the traditional online recommendation system, much depends on thecollaborative filtering recommendation algorithm, however, recommend system aims to attractthe interest of consumers and turn visitors into buyers, rather than accurately predict their score.
Online recommendation system is the service version of social filtering process. Mostprevious studies emphasize the accuracy of the collaborative filtering algorithm. However, theeffective recommendation system must be credible. It requires that the system logic betransparency and the system be able to provide consumers a new, inexperienced project. Basedon the above, this paper proposes to research the quality evaluation of recommendation systemfrom the angle of user’s experience, adding a freshness parameters of Top-N recommendcollaborative filtering similarity calculation method, and comparing with the classicalrecommended algorithm. The result of this experiment has a certain degree of accuracy and highdiversity, which provides basis for establishing the e-commercial recommendation system.
4. Putting forward the emotion text recognition algorithm based on maximum entropytheory
The large number of consumers text reviews information has great influence on otherconsumers perception of enterprise’s public praise and reputation, hiding a lot of consumer’spreferences and behavior characteristics. Using the text reviews information analysis technology,the enterprises could know the perceived service quality and preferences of their reputation,goods and services from consumers.
This paper put forward an online review emotional collocation recognition algorithm basedon maximum entropy model to recognize the emotional tendency of online reviews. Thisresearch designs maximum entropy model based on semantic features of sentimental words,regarding the synonyms in emotion text review corpus as a semantic feature class according tothe synonym lists, choosing the emotion text review corpus which contains a certain semantic feature to construct atomic feature template and composite feature template based on maximumentropy model, thus for automatic identification. The judgment on the collocation relationshipbetween evaluation object and evaluative words in emotion text review corpus is a dichotomy ofcollocation and non-collocation. This paper dichotomizes the evaluation of collocation based onmaximum entropy model and expands the maximum entropy feature template by establishingpolarity glossary. The experiment proves that the model can improve the accuracy ofclassification.
在线消费者行为的研究综合了社会心理学、计算机科学、经济学、营销学、行为科学、物理学以及复杂网络等学科，研究的目标是指导计算机网络技术特别是电子商务更好的为社会发展服务。大量企业的协同过滤推荐系统通过建立消费者预测模型，提供个性化推荐服务，解决信息过载等问题。本文基于上述需求，深入研究了服务理论研究进展和服务质量管理理论，对服务科学的研究起源、研究现状、研究方法进行了全面分析，并深刻挖掘了服务科学研究目前存在的问题，运用现代营销学中的服务质量评价和满意度理论，构建了服务主导逻辑的理论研究框架，为企业提高服务创新能力提供实践指导，并为系统规划提供了理论方向，主要内容包括以下四点：
1、提出消费者购买决策行为研究模型
基于初始信任和TAM理论，通过访谈和问卷调查，对消费者基于在线电子供应商的首次购买行为进行实证研究。为增强消费者对电子供应商的首次购买意图，加入一个新的变量，即对电子供应商的感知服务质量，它直接影响首次购买意图，根据分析结果提出了一个垂直的理论体系。
在这个理论体系下，初始信任的建立和感知服务质量理论对新的在线消费者首次购买意图非常关键，由数据分析结果发现了各种影响因素对消费者意图行为的作用，提醒企业在规划网站在线购物系统各构成要素时要重视对于初始信任的建立，而消费者的感知服务质量也不容忽视。
2、分析电子商务中长尾理论并将其应用到电影推荐中
长尾理论认为随着信息技术的发展，经济发展的趋势是从纯原子经济学到字节和原子的混合然后成为纯字节的经济，通过对大数据中潜在信息的分析可以推荐出用户真正满意的产品和服务。
不存在能够适用于所有产品的推荐系统，只有用户深度参与的个性化推荐才能够根据碎片化的信息在一定的准确度和一定的多样性上猜测消费者的偏好，并据此向用户推荐，这些个性化推荐技术不仅能节省用户浏览搜索的时间，并且能找到一些网络角落里的暗信息，挖掘到信息海洋中的长尾利润。本文在深入总结了长尾理论相关问题的基础上，分析了长尾理论在电影推荐中的应用。
3、提出并验证了基于多样性的协同过滤推荐模型
传统的在线推荐系统的准确性多依赖于协同过滤推荐算法，然而推荐系统的目的是吸引消费者的兴趣，将浏览者转变为购买者，而不是准确的预测他们的评分。
在线推荐系统是社会过滤过程的服务器版本，前期的研究大多强调协同过滤算法的精确性。而有效的推荐系统必须是可信任的，而且要求系统逻辑透明度好，能够提供给消费者新的、无经验的项目。本文基于此提出从用户体验的视角研究推荐系统的质量评价，在推荐候选集合算法中增加了一个新鲜度因子，利用协同过滤相似度计算方法结合聚簇算法进行个性化推荐，推荐结果与传统的推荐算法进行比较，实验结果具有一定的准确度和较高的多样性，从而为更好的建立电子商务推荐系统提供了依据。
4、提出基于最大熵的情感文本识别算法
由于互联网上海量的消费者文本评论信息对其他消费者感知企业的口碑信誉有重要的影响，而且蕴藏了大量消费者的偏好和行为特征，通过文本评论信息分析技术，企业可以了解消费者对企业信誉、产品或服务的感知服务质量和个性化偏好，从而有针对性地制定企业战略，改进信息系统体系结构，提供个性化推荐项目，提高竞争实力。
本文提出了一种基于最大熵模型的在线评论情感搭配识别算法，用来识别网络文本评论的情感倾向。设计了基于情感词语语义特征的最大熵模型算法，根据同义词词表将情感文本评论语料中的同义词或近义词词语作为一个语义特征类，选择包含某一语义特征类的情感文本评论语料组成基于最大熵的原子特征模板和复合特征模板，进行自动的识别。对文本情感评论语料中评价词和评价对象之间搭配关系的判断属于一个搭配或非搭配的二分类问题。本文基于最大熵模型对评价搭配进行二分类，并通过构建极性词表以扩充最大熵特征模板，实验证明该模型能够提高分类的准确率。
With the rapid popularization of Internet application in commercial field, the consumer’sdemand becomes the pilot which drives the development of Internet. Meanwhile, the socialnetwork represented by e-commerce varies constantly. In the e-commerce which is highlyintegrated of computer system and social interaction system, the consumers become more andmore important; the consumers’ behavior characteristics and their potential needs graduallybecome the important power to impel the development of technology. Therefore, to researchconsumers decision behavior, to analyze consumers information characteristics, as well as todiscover the underlying rules in consumer behavior and the relational degree between consumers,could guide the computer network develop better and make e-commerce develop towards thedirection of more intelligent and more human, thus mix together with the real social system moreharmoniously.
The research on online consumer behavior closely links with many subjects, such as, socialpsychology, computer science, economics, marketing, anthropology, physics and everything todo with complex network. The ultimate goal is to guide the computer network technology,especially e-commerce, providing a better service to the development of human society. A largenumber of enterprises’ collaborative filtering recommendation systems solve such problems asinformation overload, through establishing consumer prediction model and providingpersonalized service. Based on the above requirements, this paper goes further into the researchprogress of service theory and the service quality management theory, analyzingcomprehensively the research origin, research status and research method of service science,excavating profoundly the existing problems of the study on service science. With the servicequality evaluation and satisfaction theory in modern marketing, this research builds thetheoretical research framework serving the dominant logic, providing practical guidance forenterprises to improve the service innovation ability and theoretical direction for systemplanning. The research content includes the following aspects.
1. Presenting the research model on consumers buying decision behavior
Based on the initial trust and TAM theory, this paper gives the empirical research on theconsumers first time buying behavior based on the online electronic supplier through interviewsand questionnaire survey. In order to enhance consumer’s intention of the first time buying of theelectronic supplier, the research adds a new variable, namely the perceived service quality for theelectronic suppliers which influence directly the first time purchase intention. According to theresults of the analysis, the research puts forward a vertical theory system.
In this theory system, the establishment of initial trust and perceived service quality theoryare critical for the new online consumer’s intention of the first time purchase. According to theresult of the statistical analysis, this research finds the effect of various kinds of influence factorson consumers’ intention behavior, reminding the enterprises to pay attention to the establishmentof initial trust in projecting the websites elements of online shopping system, while consumersperceived service quality cannot be neglected.
2. Analyzing the long tail theory in electronic commerce and applying it to the movierecommendation
In the long tail theory, with the development of information technology, the trend ofeconomic development is from a mixture of pure economics to byte and atoms, then to theeconomy of pure bytes. It could recommend the really satisfying products and services to theusers through the analysis of the potential information in big data.
There is no recommendation system which could be applied to all products. Only the userswho are deeply involved in the personalized recommendation, can the system speculates theirpreferences in a certain accuracy and diversity according to the fragments of information, andrecommends to the user according to this. The personalized recommendation technology can notonly save users browse search time, but also can find some potential information in the corner ofthe network, digging out the profit from the long tail theory in the information ocean. On thebasis of deep summary of related issues of the long tail theory, this paper analyzes the long tailtheory application in movie recommendations.
3. Proposing and verifying the collaborative filtering recommendation model based ondiversity
The accuracy of the traditional online recommendation system, much depends on thecollaborative filtering recommendation algorithm, however, recommend system aims to attractthe interest of consumers and turn visitors into buyers, rather than accurately predict their score.
Online recommendation system is the service version of social filtering process. Mostprevious studies emphasize the accuracy of the collaborative filtering algorithm. However, theeffective recommendation system must be credible. It requires that the system logic betransparency and the system be able to provide consumers a new, inexperienced project. Basedon the above, this paper proposes to research the quality evaluation of recommendation systemfrom the angle of user’s experience, adding a freshness parameters of Top-N recommendcollaborative filtering similarity calculation method, and comparing with the classicalrecommended algorithm. The result of this experiment has a certain degree of accuracy and highdiversity, which provides basis for establishing the e-commercial recommendation system.
4. Putting forward the emotion text recognition algorithm based on maximum entropytheory
The large number of consumers text reviews information has great influence on otherconsumers perception of enterprise’s public praise and reputation, hiding a lot of consumer’spreferences and behavior characteristics. Using the text reviews information analysis technology,the enterprises could know the perceived service quality and preferences of their reputation,goods and services from consumers.
This paper put forward an online review emotional collocation recognition algorithm basedon maximum entropy model to recognize the emotional tendency of online reviews. Thisresearch designs maximum entropy model based on semantic features of sentimental words,regarding the synonyms in emotion text review corpus as a semantic feature class according tothe synonym lists, choosing the emotion text review corpus which contains a certain semantic feature to construct atomic feature template and composite feature template based on maximumentropy model, thus for automatic identification. The judgment on the collocation relationshipbetween evaluation object and evaluative words in emotion text review corpus is a dichotomy ofcollocation and non-collocation. This paper dichotomizes the evaluation of collocation based onmaximum entropy model and expands the maximum entropy feature template by establishingpolarity glossary. The experiment proves that the model can improve the accuracy ofclassification.
引文
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