Inter-Viewing the Amazon Web Salespersons: Trends, Complementarities and Competition

Vafopoulos, Michalis; Theodoridis, Thomas; Kontokostas, Dimitris

doi:10.2139/ssrn.1850360

Cited by 3 publications

(11 citation statements)

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“…[1]) and micro scale (e.g. [2], [3]) has become a major task for computer and social scientists. The trans-disciplinary field in this direction has been entitled "Web Science" and is focused in the significant reciprocal relationship among the social interactions enabled by the Web's design, the scalable and open applications development mandated to support them, and the architectural and data requirements of these large-scale applications [4], [5], [6].…”

Section: Prefacementioning

confidence: 99%

“…tags, playlists, collaborative filtering) and (b) directly, by increasing consumption gains (e.g. discovery of complementary goods in co-purchase networks [19]). Actually, what connected consumers create is not simply content (e.g.…”

Section: More Energetic and Connected Consumptionmentioning

confidence: 99%

“…The User will be recommended Web Goods similar to her past preferences [28]. In the case of content-based recommendations the two-dimensional matrix R CB is given by the following representation: R CB : User id x Web Good feat  rating (2) where User id is a unique identifier of the Users, Web Good feat refers to a feature space that represents the Web Good's features and rating reflects the User's evaluation for the Web Good's features [26].…”

Section: A Content-based Recommendationsmentioning

confidence: 99%

“…Specifically, the above classification refers to the availability of data in the N x M User-Web Good rating matrix. In the first case of ratings data model, each User provides explicit feedback by reporting a vote for a subset of Web Goods on a numerical (e.g., [1][2][3][4][5][6] or ordinal (e.g., like, indifferent or dislike) scale (Table 1). The binary data is considered to be a truncated version of the first model since Users express either a positive or a negative feedback.…”

Section: The Numerical Values Of Implicit Feedback Declare Confidence...mentioning

confidence: 99%

“…Salespersons in the physical world are responsible of making product recommendations to customers, which are integrated and aligned with the firm's marketing strategy. According to the Chartered Institute of Marketing, Marketing is the management process, which fulfills the following objectives: identifying, anticipating and satisfying customer requirements profitably 2 .…”

Section: Introductionmentioning

confidence: 99%

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Recommendation Systems: Bridging Technical Aspects with Marketing Implications

Vafopoulos

Oikonomou

2013

Semantic Hyper/Multimedia Adaptation

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In 2010, Web users ordered, only in Amazon, 73 items per second and massively contribute reviews about their consuming experience. As the Web matures and becomes social and participatory, collaborative filters are the basic complement in searching online information about people, events and products.In Web 2.0, what connected consumers create is not simply content (e.g. reviews) but context. This new contextual framework of consumption emerges through the aggregation and collaborative filtering of personal preferences about goods in the Web in massive scale. More importantly, facilitates connected consumers to search and navigate the complex Web more effectively and amplifies incentives for quality.The objective of the present article is to jointly review the basic stylized facts of relevant research in recommendation systems in computer and marketing studies in order to share some common insights.After providing a comprehensive definition of goods and Users in the Web, we describe a classification of recommendation systems based on two families of criteria: how recommendations are formed and input data availability. The classification is presented under a common minimal matrix notation and is used as a bridge to related issues in the business and marketing literature. We focus our analysis in the fields of one-to-one marketing, network-based marketing Web merchandising and atmospherics and their implications in the processes of personalization and adaptation in the Web. Market basket analysis is investigated in context of recommendation systems. Discussion on further research refers to the business implications and technological challenges of recommendation systems. 1

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Section: Prefacementioning

confidence: 99%

Section: More Energetic and Connected Consumptionmentioning

confidence: 99%

Section: A Content-based Recommendationsmentioning

confidence: 99%

Section: The Numerical Values Of Implicit Feedback Declare Confidence...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recommendation Systems: Bridging Technical Aspects with Marketing Implications

Vafopoulos

Oikonomou

2013

Semantic Hyper/Multimedia Adaptation

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The Web Economy: Goods, Users, Models, and Policies

Vafopoulos

2011

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The Web Economy: Goods, Users, Models and Policies

Vafopoulos

2012

SSRN Journal

Self Cite

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Web emerged as an antidote to the rapidly increasing quantity of accumulated knowledge and become successful because it facilitates massive participation and communication with minimum costs. Today, its enormous impact, scale and dynamism in time and space make very difficult (and sometimes impossible) to measure and anticipate the effects in human society. In addition to that, we demand from the Web to be fast, secure, reliable, all-inclusive and trustworthy in any transaction. The scope of the present article is to review a part of the Web economy literature that will help us to identify its major participants and their functions. The goal is to understand how the Web economy differs from the traditional setting and what implications have these differences. Secondarily, we attempt to establish a minimal common understanding about the incentives and properties of the Web economy. In this direction the concept of Web Goods and a new classification of Web Users are introduced and analyzed This article, is not, by any means, a thorough review of the economic literature related to the Web. We focus only on its relevant part that models the Web as a standalone economic artifact with native functionality and processes.

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Inter-Viewing the Amazon Web Salespersons: Trends, Complementarities and Competition

Cited by 3 publications

References 16 publications

Recommendation Systems: Bridging Technical Aspects with Marketing Implications

Recommendation Systems: Bridging Technical Aspects with Marketing Implications

The Web Economy: Goods, Users, Models, and Policies

The Web Economy: Goods, Users, Models and Policies

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