网络推荐系统的营销研究
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摘要
互联网企业在上世纪90年代末得到了非理性地狂热追捧。那时,有大量的投资者和企业家都对网络企业表现出了极大的兴趣。然而,2001年的网络企业寒冬则充分体现了市场的客观性,大量的新兴网络企业在这个寒冬中倒下。但是,倒下的却不是网络给经济发展带来的契机,而是给投资者们过热的脑袋降温,让他们能够静心冷静地看待网络的发展。
诸如Google, Yahoo!, E-bay, Amazon等经历网络寒冬洗礼的网络企业的发展和成功充分说明了互联网正在深刻地改变着组织和个人互相交流和交换产品的方式,改变着市场运行的方式。互联网正在创造一个以产品和服务的电子交易为特色的市场,在它孵养下的全球经济正呈现出以下特点:1)网络经济欣欣向荣;2)它在给我们带来产品极大丰富的同时也让我们不得不承受着信息过载的负面影响;3)大众化定制时代来临。
网络的发展,信息的爆炸式增长,不仅加大了消费者购买决策的难度,也使得营销者(商家)越来越难以确切地把握顾客的兴趣偏好,并提供合适的市场供应品。推荐系统,作为一种智能系统,能够很好的解决上述问题。对于营销者而言,推荐系统的应用可以实现低成本和个性化营销,对消费者而言,则可以借助推荐系统的辅助以降低其决策所付出努力,提升其决策质量。
然而,尽管推荐系统对于营销存在着如此深刻的影响和重要的作用,此前的学者研究却没有能够对推荐系统的营销功用展开充分的探讨。从上世纪90年代初第一个真正意义上的推荐系统--- Tapestry---出现以来,学者们对其做了很多的研究,但是几乎所有的相关研究都着眼于如何处理已经获得的个人偏好等信息,也即把研究重点放在了如何发展和评价某种推荐系统算法。迄今为止,关于推荐系统的文献主要关注产生推荐的算法研究(主要是关于精确度和代表性的原则)和现有推荐系统的分类(主要基于推荐系统采用的算法和技术),而忽视了其它方面的内容,比如对推荐系统的营销原理、消费者行为/心理原理的关注就显得很不足。但是从2000年Asim Ansari等人对推荐系统进行的营销方面的开拓性研究工作开始,大量的学者给予了消费者行为/心理以及营销因素更多的关注,将它们作为影响推荐系统最终推荐效果的一个部分通过实证研来加以研究。自此,从营销和消费者心理原理角度探索和研究推荐系统可能带来的营销影响成为相关研究的重点之一,相当多的学者从各种角度对其进行了论证:如H?ubl和Trifts(2000)认为推荐系统能够降低消费者做出消费购买决策所付出的努力;Swaminathan(2003)则认为商品的类别风险和复杂性会降低推荐系统的效用;Aggarwal和Vaidyanathan(2005)的研究表明推荐系统对不同类别商品有不同的推荐效果;Nikolaeva和S. Sriram(2006)则研究论证表明消费者的特点、推荐建议接受者的特点以及产品属性三者会影响到人们对推荐系统的接受程度等等。推荐系统的营销相关研究已经进入了一个百花争鸣的阶段。
尽管如此,推荐系统的相关研究学者却始终没有有效地对推荐系统的效用原理(即为什么推荐系统能够影响到消费者,它背后的原理是什么)做出一番诠释,而仅就个别营销因素和消费者行为/心理因素和推荐系统的相关性做了许多的实证研究。本文则从营销中的“口碑”效应入手,认为推荐系统背后所倚靠的营销原理就是一种代表大众智慧的“口碑”,“推荐”在本质上属于口碑的一种形式。并同时简单揭示出了人们提供口碑(推荐)的动机,提供了一个推荐模型。
此外,推荐系统相关研究正处于一个高速的扩展期,有相当多的学者从各种角度研究了消费者行为/心理和营销因素与推荐系统效果间的相关性,该领域内的科研成果也日渐丰富。然而,丰富的成果却缺乏有效的归纳和组织,以形成一个较为清晰的成果框架,从而方便后续的研究学者能够减少可能的重复研究,转而倾注心力于“未知”领域。Bo Xiao和Izak Benbasat(2007)成为作出该贡献的第一人,他们较为系统地将已有的成果归纳为了推荐系统特点、产品特点、推荐系统使用、使用者特点和使用者—推荐系统互动等5个方面。本文在此基础之上,结合营销理论,构建了推荐系统的营销效果评价模型,并揭示了模型各因素之间的作用机制。
在最后一章,文章考察了推荐系统在现实中的应用。着重考虑了其在被称之为“长尾企业”的网络公司中的巨大潜力。
文章主要目的在于实现对网络推荐系统的综合研究。在参考文献的基础上实现对推荐系统的全方面了解,并藉此提出一个评价和分析推荐系统营销效果的综合模型,完成文章的主体部分。为了使本研究内容更有现实意义,文章的最后部分结合实际阐述了推荐系统在长尾企业中所展现的巨大商业营销潜力。文章主要分四部分,每章主要内容如下:
第一章为绪论。这部分点明了本研究的目的和意义,着重参详了推荐系统相关的国内外研究,为下文整理评价模型和分析因素影响机制做铺垫。
第二章为推荐和推荐系统。该部分从研究消费行为中的推荐行为开始,为进一步认识和完善推荐系统提供理论支持,最后以指出现行推荐系统面临的主要问题结束。
第三章为推荐系统的效果评价模型分析,是本文的主体部分。在这部分内容中,通过建立评价指标和萃取效果影响因素,文章最终建立起了一个涉及两大评价指标和六大影响因素的推荐系统效果评价模型。并且通过分析阐述了各因素对推荐系统最终效果的影响机制。
第四章为推荐系统的一个实例。这部分把推荐系统和长尾现象结合起来说明推荐系统所蕴含的巨大影响潜力。通过对Netflix的DVD推荐系统的实例分析,从三方面展示了推荐系统对于提升顾客满意,促进非热门产品的销售以及增加企业利润的积极作用。
本文通过:1)从营销角度研究了网络推荐系统对消费者决策和现代企业,尤其是网络企业的营销带来的影响;2)研究分析整理出一个推荐系统的营销效果的综合评价模型,等两个主要方面对前人的相关研究作出了一定的创新。
然而,鉴于推荐系统相关研究正处于一个快速发展的时期,加之笔者能力有限,本文章可能存在以下不足:1)对推荐系统的整体研究可能尚不全面;2)未对推荐系统评价模型中两大指标存在的关系以及其所内涵的变量之间的关系进行进一步深入探讨;3)对推荐系统营销效果产生影响的因素选择不完全等。
The internet is changing our ways of communication and trading. It is creating a global market of e-business. The economics of our planet is characterizing itself as follows with the help of internet:1) the internet economics is blossoming; 2) we have to undertake information overload while we are enjoying the tremendous choices the internet has brought to us; 3) the era of mass customization is coming with the growth of internet.
Both the development of internet economics and the bursting increase of information not only harden the difficulty consumers have in making consumption decisions, but also make it harder for marketers to know what customers exactly want and provide proper offerings. While, as a intelligent agent, recommendation systems are able to get those problems well solved. As to marketers, using recommendation systems could have a low marketing cost and customized marketing. For consumers, we could save the effort that is needed to make a decision, and have our decision quality improved with the help of recommendation systems.
Although recommendation systems have so great influence in marketing, former researchers have not paid much attention to its marketing function. Since the beginning of 1990’s when the first reco-systems showed up, lost of researchers have done loads of studies about them, but almost all of the studies put their concerns on how to process the collected consumer data, that is, how to develop a better algorithm. Many researches could be regarded as studies in computer science and technology field, and very few can be directly and effectively used by marketers. This comes to a landmark change in 2000 when Asim Ansari et. Al first studied the reco-systems from the point of marketing. From then on, researches of recommendation systems on marketing and consumer behavior find their way to a more important field.
Nevertheless, all these researches have not explained why and how the recommendation systems influence consumers. They only did lots of empirical studies on the relationship between systems and marketing or consumer related factors. This paper tries to indicate that word-of-mouth is the marketing principles recommendation systems have to influence consumers, and provides a model of recommendation.
Years of research produce tons of results, and these help us to understand recommendation agents from many ways. While these fruits are hardly concluded and organized, so it is not easy for subsequent researchers to go over all studied aspects and avoid possible repeats. Bo Xiao and Izak Benbasat are the first persons who did the job in the early 2007. They summarized five factors to help understand the whole research. This paper builds an evaluating model of reco-systems’marketing influence, and meanwhile, reveals the mechanism that all factors has to affect each other.
In the last charter of the thesis, we inspect the application of recommendation systems in reality, and we focus on the phenomenon of The Longtail.
The main purpose of the thesis is to have a synthesized study of recommendation systems. It helps us to have a general understanding of recommendation systems, and then forms a evaluating model for its marketing influence based on the literature reviews. To make the research more helpful in reality, the last part of the thesis discusses about the application of recommendation systems in Longtail companies. The paper is divided into four parts, and each part is briefly introduced as follows:
The first part is Introduction. This part tells the purpose and meanings of the dissertation, and it concludes the literature reviews about recommendation systems. It helps to form the foundation of the rest parts of the paper.
The second part is Recommendation and Recommendation Systems. This part studies the recommendation in consumer behaviors, and thus helps to reveal the marketing principles of recommendation systems. It ends with indicating the main problems that recommendation systems have nowadays.
The third part is The Analysis of The Evaluating Model of Recommendation Systems’Marketing Influence. It is the most important part of the paper. In this part, we draw two main evaluating index and six affecting factors to form the model, and also analyze the mechanism how factors affect the ultimate marketing influence of recommendation system.
The forth part, the last part, looks into the application of recommendation systems in reality, and with a real example, we reveal the positive role the recommendation systems play in helping improve customer satisfaction, sales of unfamiliar products and increase the profit of companies.
诸如Google, Yahoo!, E-bay, Amazon等经历网络寒冬洗礼的网络企业的发展和成功充分说明了互联网正在深刻地改变着组织和个人互相交流和交换产品的方式,改变着市场运行的方式。互联网正在创造一个以产品和服务的电子交易为特色的市场,在它孵养下的全球经济正呈现出以下特点:1)网络经济欣欣向荣;2)它在给我们带来产品极大丰富的同时也让我们不得不承受着信息过载的负面影响;3)大众化定制时代来临。
网络的发展,信息的爆炸式增长,不仅加大了消费者购买决策的难度,也使得营销者(商家)越来越难以确切地把握顾客的兴趣偏好,并提供合适的市场供应品。推荐系统,作为一种智能系统,能够很好的解决上述问题。对于营销者而言,推荐系统的应用可以实现低成本和个性化营销,对消费者而言,则可以借助推荐系统的辅助以降低其决策所付出努力,提升其决策质量。
然而,尽管推荐系统对于营销存在着如此深刻的影响和重要的作用,此前的学者研究却没有能够对推荐系统的营销功用展开充分的探讨。从上世纪90年代初第一个真正意义上的推荐系统--- Tapestry---出现以来,学者们对其做了很多的研究,但是几乎所有的相关研究都着眼于如何处理已经获得的个人偏好等信息,也即把研究重点放在了如何发展和评价某种推荐系统算法。迄今为止,关于推荐系统的文献主要关注产生推荐的算法研究(主要是关于精确度和代表性的原则)和现有推荐系统的分类(主要基于推荐系统采用的算法和技术),而忽视了其它方面的内容,比如对推荐系统的营销原理、消费者行为/心理原理的关注就显得很不足。但是从2000年Asim Ansari等人对推荐系统进行的营销方面的开拓性研究工作开始,大量的学者给予了消费者行为/心理以及营销因素更多的关注,将它们作为影响推荐系统最终推荐效果的一个部分通过实证研来加以研究。自此,从营销和消费者心理原理角度探索和研究推荐系统可能带来的营销影响成为相关研究的重点之一,相当多的学者从各种角度对其进行了论证:如H?ubl和Trifts(2000)认为推荐系统能够降低消费者做出消费购买决策所付出的努力;Swaminathan(2003)则认为商品的类别风险和复杂性会降低推荐系统的效用;Aggarwal和Vaidyanathan(2005)的研究表明推荐系统对不同类别商品有不同的推荐效果;Nikolaeva和S. Sriram(2006)则研究论证表明消费者的特点、推荐建议接受者的特点以及产品属性三者会影响到人们对推荐系统的接受程度等等。推荐系统的营销相关研究已经进入了一个百花争鸣的阶段。
尽管如此,推荐系统的相关研究学者却始终没有有效地对推荐系统的效用原理(即为什么推荐系统能够影响到消费者,它背后的原理是什么)做出一番诠释,而仅就个别营销因素和消费者行为/心理因素和推荐系统的相关性做了许多的实证研究。本文则从营销中的“口碑”效应入手,认为推荐系统背后所倚靠的营销原理就是一种代表大众智慧的“口碑”,“推荐”在本质上属于口碑的一种形式。并同时简单揭示出了人们提供口碑(推荐)的动机,提供了一个推荐模型。
此外,推荐系统相关研究正处于一个高速的扩展期,有相当多的学者从各种角度研究了消费者行为/心理和营销因素与推荐系统效果间的相关性,该领域内的科研成果也日渐丰富。然而,丰富的成果却缺乏有效的归纳和组织,以形成一个较为清晰的成果框架,从而方便后续的研究学者能够减少可能的重复研究,转而倾注心力于“未知”领域。Bo Xiao和Izak Benbasat(2007)成为作出该贡献的第一人,他们较为系统地将已有的成果归纳为了推荐系统特点、产品特点、推荐系统使用、使用者特点和使用者—推荐系统互动等5个方面。本文在此基础之上,结合营销理论,构建了推荐系统的营销效果评价模型,并揭示了模型各因素之间的作用机制。
在最后一章,文章考察了推荐系统在现实中的应用。着重考虑了其在被称之为“长尾企业”的网络公司中的巨大潜力。
文章主要目的在于实现对网络推荐系统的综合研究。在参考文献的基础上实现对推荐系统的全方面了解,并藉此提出一个评价和分析推荐系统营销效果的综合模型,完成文章的主体部分。为了使本研究内容更有现实意义,文章的最后部分结合实际阐述了推荐系统在长尾企业中所展现的巨大商业营销潜力。文章主要分四部分,每章主要内容如下:
第一章为绪论。这部分点明了本研究的目的和意义,着重参详了推荐系统相关的国内外研究,为下文整理评价模型和分析因素影响机制做铺垫。
第二章为推荐和推荐系统。该部分从研究消费行为中的推荐行为开始,为进一步认识和完善推荐系统提供理论支持,最后以指出现行推荐系统面临的主要问题结束。
第三章为推荐系统的效果评价模型分析,是本文的主体部分。在这部分内容中,通过建立评价指标和萃取效果影响因素,文章最终建立起了一个涉及两大评价指标和六大影响因素的推荐系统效果评价模型。并且通过分析阐述了各因素对推荐系统最终效果的影响机制。
第四章为推荐系统的一个实例。这部分把推荐系统和长尾现象结合起来说明推荐系统所蕴含的巨大影响潜力。通过对Netflix的DVD推荐系统的实例分析,从三方面展示了推荐系统对于提升顾客满意,促进非热门产品的销售以及增加企业利润的积极作用。
本文通过:1)从营销角度研究了网络推荐系统对消费者决策和现代企业,尤其是网络企业的营销带来的影响;2)研究分析整理出一个推荐系统的营销效果的综合评价模型,等两个主要方面对前人的相关研究作出了一定的创新。
然而,鉴于推荐系统相关研究正处于一个快速发展的时期,加之笔者能力有限,本文章可能存在以下不足:1)对推荐系统的整体研究可能尚不全面;2)未对推荐系统评价模型中两大指标存在的关系以及其所内涵的变量之间的关系进行进一步深入探讨;3)对推荐系统营销效果产生影响的因素选择不完全等。
The internet is changing our ways of communication and trading. It is creating a global market of e-business. The economics of our planet is characterizing itself as follows with the help of internet:1) the internet economics is blossoming; 2) we have to undertake information overload while we are enjoying the tremendous choices the internet has brought to us; 3) the era of mass customization is coming with the growth of internet.
Both the development of internet economics and the bursting increase of information not only harden the difficulty consumers have in making consumption decisions, but also make it harder for marketers to know what customers exactly want and provide proper offerings. While, as a intelligent agent, recommendation systems are able to get those problems well solved. As to marketers, using recommendation systems could have a low marketing cost and customized marketing. For consumers, we could save the effort that is needed to make a decision, and have our decision quality improved with the help of recommendation systems.
Although recommendation systems have so great influence in marketing, former researchers have not paid much attention to its marketing function. Since the beginning of 1990’s when the first reco-systems showed up, lost of researchers have done loads of studies about them, but almost all of the studies put their concerns on how to process the collected consumer data, that is, how to develop a better algorithm. Many researches could be regarded as studies in computer science and technology field, and very few can be directly and effectively used by marketers. This comes to a landmark change in 2000 when Asim Ansari et. Al first studied the reco-systems from the point of marketing. From then on, researches of recommendation systems on marketing and consumer behavior find their way to a more important field.
Nevertheless, all these researches have not explained why and how the recommendation systems influence consumers. They only did lots of empirical studies on the relationship between systems and marketing or consumer related factors. This paper tries to indicate that word-of-mouth is the marketing principles recommendation systems have to influence consumers, and provides a model of recommendation.
Years of research produce tons of results, and these help us to understand recommendation agents from many ways. While these fruits are hardly concluded and organized, so it is not easy for subsequent researchers to go over all studied aspects and avoid possible repeats. Bo Xiao and Izak Benbasat are the first persons who did the job in the early 2007. They summarized five factors to help understand the whole research. This paper builds an evaluating model of reco-systems’marketing influence, and meanwhile, reveals the mechanism that all factors has to affect each other.
In the last charter of the thesis, we inspect the application of recommendation systems in reality, and we focus on the phenomenon of The Longtail.
The main purpose of the thesis is to have a synthesized study of recommendation systems. It helps us to have a general understanding of recommendation systems, and then forms a evaluating model for its marketing influence based on the literature reviews. To make the research more helpful in reality, the last part of the thesis discusses about the application of recommendation systems in Longtail companies. The paper is divided into four parts, and each part is briefly introduced as follows:
The first part is Introduction. This part tells the purpose and meanings of the dissertation, and it concludes the literature reviews about recommendation systems. It helps to form the foundation of the rest parts of the paper.
The second part is Recommendation and Recommendation Systems. This part studies the recommendation in consumer behaviors, and thus helps to reveal the marketing principles of recommendation systems. It ends with indicating the main problems that recommendation systems have nowadays.
The third part is The Analysis of The Evaluating Model of Recommendation Systems’Marketing Influence. It is the most important part of the paper. In this part, we draw two main evaluating index and six affecting factors to form the model, and also analyze the mechanism how factors affect the ultimate marketing influence of recommendation system.
The forth part, the last part, looks into the application of recommendation systems in reality, and with a real example, we reveal the positive role the recommendation systems play in helping improve customer satisfaction, sales of unfamiliar products and increase the profit of companies.
引文
1 Zabihollah Rezaee, Kenneth r, Lambert & w. ken Harmon(2006), Accounting Education. Vol. 15, No. 1, 73–88.
2 http://news.xinhuanet.com/internet/2007-06/22/content_6275831.htm
3 http://news.xinhuanet.com/tech/2006-11/03/content_5285836.htm
4 http://www.forrester.com/Research/Document/Excerpt/0,7211,34576,00.html.
1 Chih-Ping Wei, Michael J. Shaw and Robert F. Easley (2001), A Survey of Recommendation Systems in Electronic Commerce.
2 http://news.xinhuanet.com/newmedia/2006-10/16/content_5209269.htm
3 Mark van Setten (2002), Experiments with A Recommendation Technique That Learns Category Interests.
4 Loren Terveen (2001), Will Hill. Beyond Recommendation Systems: Helping People Help Each Other.
5 S. Narayanan, Ph.D., P.E., Lavanyakoppaka, M.S., Narasimha Edala, M.S., Don loritz, Ph.D., and Raymond Daley, M.A.(2004), Adaptive Interface for Personalizing Information Seeking. CYBER PSYCOLOGY & BEHAVIOR. Volume 7.
1 http://news.xinhuanet.com/newmedia/2006-10/16/content_5209269.htm
2黄升民、杨雪睿.《碎片化背景下的分众传播与新媒体的发展》[J].《广告主》,2006(5):34-37.
1当当和卓越曾就谁是“全球最大网上书店”发生争论,当当发布声明指出:请卓越网停止发布谎言声称自己是“全球最大的中文网上书店”。当当以用户认知度、销售规模、商品数量三个方面的公开数据,试图说明卓越所说为谎言。
2 http://www.dangdang.com/dd_profile.asp
3 http://www.taobao.com/about/intro.php
4 http://pages.ebay.com.hk/aboutebay/thecompany/companyoverview.html
1 Huseyin Polat and Wenliang Du(2005), Privacy-Preserving Collaborative Filtering. International Jurnal of Electronic Commerce, vol. 9, No.4,pp.9-35
1菲利普·科特勒.《营销管理》[M].上海:上海人民出版社,2003年10月第1版
1 http://www.longtail.com/the_long_tail/2006/10/why_better_dvd_.htm
1 http://www.longtail.com/the_long_tail/2006/10/why_better_dvd_.htm
2 http://www.longtail.com/the_long_tail/2006/10/why_better_dvd_.htm
[1](美)菲利普·科特勒.营销管理[M].上海:上海人民出版社,2003年第1版.
[2](美)克里斯·安德森.长尾理论[M] .北京:中信出版社,2006年第1版.
[3](美)迈克尔·波特.竞争战略[M].北京:华夏出版社,2005年第1版.
[4]克莱顿·克里斯滕森、斯科特·库克、塔迪·霍尔.为营销模式纠偏[J].市场营销,2006(5),6-13
[5]黄升民、杨雪睿,碎片化背景下的分众传播与新媒体发展[J],广告主,2006(5):34-37
[6]Asim Ansari, Skander Essegaier, and Rajeev Kohli. Internet Recommendation Systems(2000), Journal of Marketing Research. Vol. XXXVII, 363-375.
[7]Andrew D. Gershoff, Ashesh Mukherjee, Anirban Mukhopadhyay(2003), Consumer Acceptance of Online Agent Advice: Extremity and Positivity Effects. Journal of Consumer Psychology. 13(1&2), 161-170.
[8]Andrews Mild, Martin Natter (2002), Collaborative filtering or regression models for Internet recommendation systems?.
[9]Alan D. .T. Cooke, Harish Sujan, Mita Sujan, Barton A. Weitz(2006), Marketing the Unfamiliar: The Role of Context and Item-Specific Information in Electronic Agent Recommendations.
[10]Alan T. Sorensen(2006). Bestseller Lists and Product Variety.
[11]Bo Xiao, Izak Benbasat (2007). E-commerce Product Recommendation Agents: Use, Characteristics, and Impact. MIS Quarterly Vol. 31 No. 1, pp. 137-209.
[12]Chih-Ping Wei, Michael J. Shaw, and Robert F. Easley (2001), A Survey of Rcommendation Systems in Electronic Commerce.
[13]Dan Ariely, John G. Lynch, Jr., Manuel Aparicio IV.(2005) Learning by Collaborative and Individual?Based Recommendation Agents. Journal of Consumer Psychology, 14(1/2), 81-95.
[14]Eliseo Reategui, John A. Campbell (2004), The Role of Personified Agents in Recommendation Delivery.
[15]Erik Brynjolfsson, Yu“Jeffrey”Hu and Michael D. Smith(2006), From Niches to Riches: Anatomy of the Long Tail. MIT Sloan Management Review, Vol.47, NO.4.
[16]Gerald Haubl, Kyle B. Murray (2003), Preference Construction and Persistence in Digital Marketplaces: The Role of Electronic Recommendation Agents. Journal of Consumer Psychology. 13(1&2), 75-91.
[17]Gavan J. Fitzsimons, Donald R. Lehmann (2004), Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses. Marketing Science. Vol. 23, No. 1, pp82-94.
[18]Gerald Haubl .Valerie Trifts (2000), Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids. Marketing Science. Vol. 19, No. 1, pp. 1-2.
[19]Huseyin Polat and Wenliang Du. Privacy-Preserving Collaborative Filtering(2005), International Journal of Electronic Commerce. Vol. 9, No. 4, pp9-35.
[20]Hong-Youl Ha.(2002), The effects of Consumer Risk Perception on Pre-purchase Information in Online Auctions: Brand, Word-of-Mouth, and Customized Information. JCMC, 8 (1).
[21]Kerimcan Ozcan (2005), Word-of-Mouth as Dialogic Discourse: A Critical Review, Synthesis, New Perspective, and Research Agenda. A doctor paper belonging to University of Michigan Business School.
[22]Loren Terveen, Will Hill (2001), Beyond Recommender Systems: Helping People Help Each Other. AT&T Labs Research.
[23]Lerzan Aksoy, Paul N. Bloom, Nicholas H.Lurie, Bruce Cooil (2006), Should Recommendation Agents Think Like People?. Journal of Service Research, Volume 8, No. 4, 297-315.
[24]Marc L. Resnick, Sheryda Pompa, Isaac Korn, and Omar Castillo. PersuasiveDesign through Intelligent Recommendation Systems.
[25]Nicholas J. Belkin (2000), Helping People Find What They Don’t know. Communication of ACM, Vol. 43, No.8.
[26]Ralitza Nikolaeva and S. Sriram. The Moderating Role of Consumer and Product Characteristics on the Value of Customized On-Line Recommendations. International Journal of Electronic Commerce, Vol. 11, No. 2, pp. 101-123.
[27]Petra Schubert, Mark Ginsburg(2000), Virtual Communities of Transaction: The Role of Personalization in Electronic Commerce. Electronic Markets, Vol.10, No.1.
[28]Praveen Aggarwal, Rajiv Vaidyanathan (2005), Perceived effectiveness of recommendation agent routines: search vs. experience goods. Int. J. Internet Marketzng and Advertising, Vol. 2, Nos. 1/2.
[29]Sherrie. X. Komiak, Izak Benbasat(2006), The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents. MIS Quarterly Vol. 30 No. 4 pp. 941-960.
[30]S. Narayanan, PH.D., P.E., Lavanyakoppaka, M.S., Narasimha Edala, M.S., Don Loritz, PH.D, and Raymond Daley, M.A.(2004), Adaptive Interface for Personalizing Information Seeking. CyberPsychology & Behavior Volume 7.
[31]Teoh Kung Keat and Dr. Avvari Mohan(2004), Multimedia University, Malaysia. Integration of TAM Based Electronic Commerce Models for Trust. The Journal of Am erican Academ y of Business, Cambridge.
[32]Ting-Peng Liang, Hung-Jen Lai, and Yi-Cheng Ku (2006), Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings. Journal of Management Information Systems, Vol. 23, No. 3, pp. 45-70.
[33]Vanitha Swaminathan(2002), The Impact of Recommendation Agents on Consumer Evaluation and Choice: The Moderating Role of Category Risk, Product Complexity, and Consumer Knowledge. JOURNAL OF CONSUMER PSYCHOLOGY, 13(1&2), 93-101.
[34]Weiquan Wang, Izak Benbasat (2005), Trust in and Adoption of Online Recommendation Agents. Journal of the Association for Information System.Vol. 6 No.3, pp.72-101.
[35]Will Hill, Larry Stead, Mark Rosenstein and George Furnas(2003), Recommending And Evaluating Choices In A Virtual Community Of Use. Computer Graphics and Interactive Media Group Home Page.
[36]Weiquan Wang and Izak Benbasat (2007), Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs. Journal of Management Information System. Spring 2007, Vol. 23, No. 4, pp. 217-246.
[37]Younghwa Lee, Kenneth A. Kozar, Kai R.T. Larsen(2001). The Technology Acceptance Model: Past, Present, and Future. Communications of the Association for Information Systems. Volume 12, Article 50 752-780.
[38]Zabihollah Rezaee, Kennethr. Lambert & W.Ken Harmon (2006), Electronic Commerce Education: Analysis of Existing Courses. Accounting Education: an international journal, Vol. 15, No. 1, 73–88.
[39]http://www.longtail.com
[40]http://www.fonerbooks.com/surfing.htm
[41]http://news.xinhuanet.com
[42]http://www.dangdang.com/dd_profile.asp
[43]http://www.taobao.com/about/intro.php
[44]http://pages.ebay.com.hk/aboutebay/thecompany/companyoverview.html
[45]http://www.amazon.com.cn
2 http://news.xinhuanet.com/internet/2007-06/22/content_6275831.htm
3 http://news.xinhuanet.com/tech/2006-11/03/content_5285836.htm
4 http://www.forrester.com/Research/Document/Excerpt/0,7211,34576,00.html.
1 Chih-Ping Wei, Michael J. Shaw and Robert F. Easley (2001), A Survey of Recommendation Systems in Electronic Commerce.
2 http://news.xinhuanet.com/newmedia/2006-10/16/content_5209269.htm
3 Mark van Setten (2002), Experiments with A Recommendation Technique That Learns Category Interests.
4 Loren Terveen (2001), Will Hill. Beyond Recommendation Systems: Helping People Help Each Other.
5 S. Narayanan, Ph.D., P.E., Lavanyakoppaka, M.S., Narasimha Edala, M.S., Don loritz, Ph.D., and Raymond Daley, M.A.(2004), Adaptive Interface for Personalizing Information Seeking. CYBER PSYCOLOGY & BEHAVIOR. Volume 7.
1 http://news.xinhuanet.com/newmedia/2006-10/16/content_5209269.htm
2黄升民、杨雪睿.《碎片化背景下的分众传播与新媒体的发展》[J].《广告主》,2006(5):34-37.
1当当和卓越曾就谁是“全球最大网上书店”发生争论,当当发布声明指出:请卓越网停止发布谎言声称自己是“全球最大的中文网上书店”。当当以用户认知度、销售规模、商品数量三个方面的公开数据,试图说明卓越所说为谎言。
2 http://www.dangdang.com/dd_profile.asp
3 http://www.taobao.com/about/intro.php
4 http://pages.ebay.com.hk/aboutebay/thecompany/companyoverview.html
1 Huseyin Polat and Wenliang Du(2005), Privacy-Preserving Collaborative Filtering. International Jurnal of Electronic Commerce, vol. 9, No.4,pp.9-35
1菲利普·科特勒.《营销管理》[M].上海:上海人民出版社,2003年10月第1版
1 http://www.longtail.com/the_long_tail/2006/10/why_better_dvd_.htm
1 http://www.longtail.com/the_long_tail/2006/10/why_better_dvd_.htm
2 http://www.longtail.com/the_long_tail/2006/10/why_better_dvd_.htm
[1](美)菲利普·科特勒.营销管理[M].上海:上海人民出版社,2003年第1版.
[2](美)克里斯·安德森.长尾理论[M] .北京:中信出版社,2006年第1版.
[3](美)迈克尔·波特.竞争战略[M].北京:华夏出版社,2005年第1版.
[4]克莱顿·克里斯滕森、斯科特·库克、塔迪·霍尔.为营销模式纠偏[J].市场营销,2006(5),6-13
[5]黄升民、杨雪睿,碎片化背景下的分众传播与新媒体发展[J],广告主,2006(5):34-37
[6]Asim Ansari, Skander Essegaier, and Rajeev Kohli. Internet Recommendation Systems(2000), Journal of Marketing Research. Vol. XXXVII, 363-375.
[7]Andrew D. Gershoff, Ashesh Mukherjee, Anirban Mukhopadhyay(2003), Consumer Acceptance of Online Agent Advice: Extremity and Positivity Effects. Journal of Consumer Psychology. 13(1&2), 161-170.
[8]Andrews Mild, Martin Natter (2002), Collaborative filtering or regression models for Internet recommendation systems?.
[9]Alan D. .T. Cooke, Harish Sujan, Mita Sujan, Barton A. Weitz(2006), Marketing the Unfamiliar: The Role of Context and Item-Specific Information in Electronic Agent Recommendations.
[10]Alan T. Sorensen(2006). Bestseller Lists and Product Variety.
[11]Bo Xiao, Izak Benbasat (2007). E-commerce Product Recommendation Agents: Use, Characteristics, and Impact. MIS Quarterly Vol. 31 No. 1, pp. 137-209.
[12]Chih-Ping Wei, Michael J. Shaw, and Robert F. Easley (2001), A Survey of Rcommendation Systems in Electronic Commerce.
[13]Dan Ariely, John G. Lynch, Jr., Manuel Aparicio IV.(2005) Learning by Collaborative and Individual?Based Recommendation Agents. Journal of Consumer Psychology, 14(1/2), 81-95.
[14]Eliseo Reategui, John A. Campbell (2004), The Role of Personified Agents in Recommendation Delivery.
[15]Erik Brynjolfsson, Yu“Jeffrey”Hu and Michael D. Smith(2006), From Niches to Riches: Anatomy of the Long Tail. MIT Sloan Management Review, Vol.47, NO.4.
[16]Gerald Haubl, Kyle B. Murray (2003), Preference Construction and Persistence in Digital Marketplaces: The Role of Electronic Recommendation Agents. Journal of Consumer Psychology. 13(1&2), 75-91.
[17]Gavan J. Fitzsimons, Donald R. Lehmann (2004), Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses. Marketing Science. Vol. 23, No. 1, pp82-94.
[18]Gerald Haubl .Valerie Trifts (2000), Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids. Marketing Science. Vol. 19, No. 1, pp. 1-2.
[19]Huseyin Polat and Wenliang Du. Privacy-Preserving Collaborative Filtering(2005), International Journal of Electronic Commerce. Vol. 9, No. 4, pp9-35.
[20]Hong-Youl Ha.(2002), The effects of Consumer Risk Perception on Pre-purchase Information in Online Auctions: Brand, Word-of-Mouth, and Customized Information. JCMC, 8 (1).
[21]Kerimcan Ozcan (2005), Word-of-Mouth as Dialogic Discourse: A Critical Review, Synthesis, New Perspective, and Research Agenda. A doctor paper belonging to University of Michigan Business School.
[22]Loren Terveen, Will Hill (2001), Beyond Recommender Systems: Helping People Help Each Other. AT&T Labs Research.
[23]Lerzan Aksoy, Paul N. Bloom, Nicholas H.Lurie, Bruce Cooil (2006), Should Recommendation Agents Think Like People?. Journal of Service Research, Volume 8, No. 4, 297-315.
[24]Marc L. Resnick, Sheryda Pompa, Isaac Korn, and Omar Castillo. PersuasiveDesign through Intelligent Recommendation Systems.
[25]Nicholas J. Belkin (2000), Helping People Find What They Don’t know. Communication of ACM, Vol. 43, No.8.
[26]Ralitza Nikolaeva and S. Sriram. The Moderating Role of Consumer and Product Characteristics on the Value of Customized On-Line Recommendations. International Journal of Electronic Commerce, Vol. 11, No. 2, pp. 101-123.
[27]Petra Schubert, Mark Ginsburg(2000), Virtual Communities of Transaction: The Role of Personalization in Electronic Commerce. Electronic Markets, Vol.10, No.1.
[28]Praveen Aggarwal, Rajiv Vaidyanathan (2005), Perceived effectiveness of recommendation agent routines: search vs. experience goods. Int. J. Internet Marketzng and Advertising, Vol. 2, Nos. 1/2.
[29]Sherrie. X. Komiak, Izak Benbasat(2006), The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents. MIS Quarterly Vol. 30 No. 4 pp. 941-960.
[30]S. Narayanan, PH.D., P.E., Lavanyakoppaka, M.S., Narasimha Edala, M.S., Don Loritz, PH.D, and Raymond Daley, M.A.(2004), Adaptive Interface for Personalizing Information Seeking. CyberPsychology & Behavior Volume 7.
[31]Teoh Kung Keat and Dr. Avvari Mohan(2004), Multimedia University, Malaysia. Integration of TAM Based Electronic Commerce Models for Trust. The Journal of Am erican Academ y of Business, Cambridge.
[32]Ting-Peng Liang, Hung-Jen Lai, and Yi-Cheng Ku (2006), Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings. Journal of Management Information Systems, Vol. 23, No. 3, pp. 45-70.
[33]Vanitha Swaminathan(2002), The Impact of Recommendation Agents on Consumer Evaluation and Choice: The Moderating Role of Category Risk, Product Complexity, and Consumer Knowledge. JOURNAL OF CONSUMER PSYCHOLOGY, 13(1&2), 93-101.
[34]Weiquan Wang, Izak Benbasat (2005), Trust in and Adoption of Online Recommendation Agents. Journal of the Association for Information System.Vol. 6 No.3, pp.72-101.
[35]Will Hill, Larry Stead, Mark Rosenstein and George Furnas(2003), Recommending And Evaluating Choices In A Virtual Community Of Use. Computer Graphics and Interactive Media Group Home Page.
[36]Weiquan Wang and Izak Benbasat (2007), Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs. Journal of Management Information System. Spring 2007, Vol. 23, No. 4, pp. 217-246.
[37]Younghwa Lee, Kenneth A. Kozar, Kai R.T. Larsen(2001). The Technology Acceptance Model: Past, Present, and Future. Communications of the Association for Information Systems. Volume 12, Article 50 752-780.
[38]Zabihollah Rezaee, Kennethr. Lambert & W.Ken Harmon (2006), Electronic Commerce Education: Analysis of Existing Courses. Accounting Education: an international journal, Vol. 15, No. 1, 73–88.
[39]http://www.longtail.com
[40]http://www.fonerbooks.com/surfing.htm
[41]http://news.xinhuanet.com
[42]http://www.dangdang.com/dd_profile.asp
[43]http://www.taobao.com/about/intro.php
[44]http://pages.ebay.com.hk/aboutebay/thecompany/companyoverview.html
[45]http://www.amazon.com.cn