Modeling Web Evolution

Vafopoulos, Michalis; Amarantidis, Efstathios; Antoniou, Ioannis

doi:10.2139/ssrn.1740603

Cited by 2 publications

(6 citation statements)

References 26 publications

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“…• The power-law exponent depends on the number α of recommended WGs by the Search Engine, the number k of topics and the number b of endorsed documents per User-Query. (Vafopoulos, Amarantidis, et al, 2011) extended the investigation for different initial random distributions of the in-degree of WGs and for different values of α and b found that the validity of the power law becomes less significant as b increases, both in the case α=b and in the case b≤α, confirming the results of .…”

Section: Equilibriumsupporting

confidence: 75%

“…The value 0 is very probable so that about k -1 of every k entries are 0. For clarity (Vafopoulos, Amarantidis, & Antoniou, 2011) (Amarantidis, Antoniou, & Vafopoulos, 2010) denoted by D td the WG components as the relevance of WG d for Topic t where t = 1,…, k is the Topic index and d=1,…, n is the WG index. "There are Users that can be thought as simple Queries asked by individuals".…”

Section: Assumptionsmentioning

confidence: 99%

“…KKPS simplify the User-Query interplay into the single entity of Users. (Vafopoulos, Amarantidis, et al, 2011) denoted by R ti the components of R t which represent the relevance of User-Query i for Topic t, where t = 1,…, k is the Topic index and i = 1,…, m is the User-Query index. The WGs are linked with the User-Query set through Search Engines.…”

Section: Assumptionsmentioning

confidence: 99%

“…According (Vafopoulos, Amarantidis, et al, 2011) the structure of the www state emerges according to the following 7-step algorithm:…”

Section: Equilibriummentioning

confidence: 99%

“…• It has been experimentally observed that WGs in-degrees are power-law distributed for a wide range of parameters. (Vafopoulos, Amarantidis, et al, 2011) have identified two main mechanisms concerning the origin of the power law distribution of the WGs in-degree: (i) Users-Queries endorse a small fraction of WGs presented b, and (ii) assuming a small fraction of multitopic WGs, the algorithm creates a high number of endorsements for them. The above mechanisms are not exhaustive for the Web graph.…”

Section: Equilibriummentioning

confidence: 99%

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The Web Economy: Goods, Users, Models and Policies

Vafopoulos

2012

SSRN Journal

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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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Section: Equilibriumsupporting

confidence: 75%

Section: Assumptionsmentioning

confidence: 99%

Section: Assumptionsmentioning

confidence: 99%

“…According (Vafopoulos, Amarantidis, et al, 2011) the structure of the www state emerges according to the following 7-step algorithm:…”

Section: Equilibriummentioning

confidence: 99%

Section: Equilibriummentioning

confidence: 99%

See 3 more Smart Citations

The Web Economy: Goods, Users, Models and Policies

Vafopoulos

2012

SSRN Journal

Self Cite

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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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Modeling Web Evolution

Cited by 2 publications

References 26 publications

The Web Economy: Goods, Users, Models and Policies

The Web Economy: Goods, Users, Models and Policies

Recommendation Systems: Bridging Technical Aspects with Marketing Implications

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