A Survey of Trust Use and Modeling in Real Online Systems

2011 ◽  
pp. 51-83 ◽  
Author(s):  
Paolo Massa
Keyword(s):  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
Online Systems ◽  
Trust Relationships ◽  
Different Types ◽  
Concept Of Information ◽  
The Web ◽  
Trust Information

This chapter discusses the concept of trust and how trust is used and modeled in online systems currently available on the Web or on the Internet. It starts by describing the concept of information overload and introducing trust as a possible and powerful way to deal with it. It then provides a classification of the systems that currently use trust and, for each category, presents the most representative examples. In these systems, trust is considered as the judgment expressed by one user about another user, often directly and explicitly, sometimes indirectly through an evaluation of the artifacts produced by that user or his/her activity on the system. We hence use the term “trust” to indicate different types of social relationships between two users, such as friendship, appreciation, and interest. These trust relationships are used by the systems in order to infer some measure of importance about the different users and influence their visibility on the system. We conclude with an overview of the open and interesting challenges for online systems that use and model trust information.

A Survey of Trust Use and Modeling in Real Online Systems

2013 ◽  
pp. 167-196
Author(s):  
Paolo Massa
Keyword(s):  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
Online Systems ◽  
Trust Relationships ◽  
Different Types ◽  
Concept Of Information ◽  
The Web ◽  
Trust Information

This chapter discusses the concept of trust and how trust is used and modeled in online systems currently available on the Web or on the Internet. It starts by describing the concept of information overload and introducing trust as a possible and powerful way to deal with it. It then provides a classification of the systems that currently use trust and, for each category, presents the most representative examples. In these systems, trust is considered as the judgment expressed by one user about another user, often directly and explicitly, sometimes indirectly through an evaluation of the artifacts produced by that user or his/her activity on the system. We hence use the term “trust” to indicate different types of social relationships between two users, such as friendship, appreciation, and interest. These trust relationships are used by the systems in order to infer some measure of importance about the different users and influence their visibility on the system. We conclude with an overview of the open and interesting challenges for online systems that use and model trust information.

ASPECTS IN BUILDING A COMPANY WEB-SITE AND ITS ROLE FOR THE DEVELOPMENT OF BUSINESS

2018 ◽  
pp. 117-124
Author(s):  
Petar Halachev ◽  
Victoria Radeva ◽  
Albena Nikiforova ◽  
Miglena Veneva
Keyword(s):  
Life Cycle ◽  
Web Site ◽  
The Internet ◽  
A Company ◽  
The Web ◽  
Design Construction ◽  
Made In ◽  
Site Types

This report is dedicated to the role of the web site as an important tool for presenting business on the Internet. Classification of site types has been made in terms of their application in the business and the types of structures in their construction. The Models of the Life Cycle for designing business websites are analyzed and are outlined their strengths and weaknesses. The stages in the design, construction, commissioning, and maintenance of a business website are distinguished and the activities and requirements of each stage are specified.

scholarly journals Machine learning: Enhancing the Perception of the Computer

2020 ◽  
Vol 9 (4) ◽  
pp. 758-761
Keyword(s):  
Machine Learning ◽  
Computer Training ◽  
Internet Traffic ◽  
The Internet ◽  
Popular Method ◽  
Exponential Increase ◽  
Computer Teaching ◽  
Traffic Detection ◽  
The Web

The advancement of technology and networking allows the use of the Web incredibly important. There is thus an exponential increase in data and information via the Internet. This flow thus is a beneficial field of study which can be defined accurately. Internet traffic detection is a very popular method of identifying information. Although so many methods have been successfully developed for classifying internet traffic, computer training technology among them is most popular. A short study of the classification of Internet traffic on various managed and non-regulated computer teaching systems was undertaken by many researchers. This paper will give various ideas to the other researcher’s and help them to learn a lot about machine learning

scholarly journals Online web navigation assistant

2021 ◽  
Vol 31 (1) ◽  
pp. 116-131
Author(s):  
N.M. Ali ◽  
A.M. Gadallah ◽  
H.A. Hefny ◽  
B.A. Novikov
Keyword(s):  
Information Overload ◽  
Specific Weight ◽  
The Internet ◽  
Fully Integrated ◽  
Web Navigation ◽  
Proposed Model ◽  
Alternative Approaches ◽  
Available Information ◽  
The Web ◽  
Online Web

The problem of finding relevant data while searching the internet represents a big challenge for web users due to the enormous amounts of available information on the web. These difficulties are related to the well-known problem of information overload. In this work, we propose an online web assistant called OWNA. We developed a fully integrated framework for making recommendations in real-time based on web usage mining techniques. Our work starts with preparing raw data, then extracting useful information that helps build a knowledge base as well as assigns a specific weight for certain factors. The experiments show the advantages of the proposed model against alternative approaches.

scholarly journals Effective Genre Classification - Understanding Url And Webpage Attributes For Classification

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2011-2016
Keyword(s):  
The Internet ◽  
Web Pages ◽  
Web Page ◽  
Exchange Method ◽  
Genre Classification ◽  
Internet Application ◽  
Reliable Classification ◽  
Time Eating ◽  
The Web

With the boom in the number of internet pages, it is very hard to discover desired records effortlessly and fast out of heaps of web pages retrieved with the aid of a search engine. there may be a increasing requirement for automatic type strategies with more class accuracy. There are a few conditions these days in which it's far vital to have an green and reliable classification of a web-web page from the information contained within the URL (Uniform aid Locator) handiest, with out the want to go to the web page itself. We want to understand if the URL can be used by us while not having to look and visit the page due to numerous motives. Getting the web page content material and sorting them to discover the genre of the net web page is very time ingesting and calls for the consumer to recognize the shape of the web page which needs to be categorised. To avoid this time-eating technique we proposed an exchange method so one can help us get the genre of the entered URL based of the entered URL and the metadata i.e., description, keywords used in the website along side the title of the web site. This approach does not most effective rely upon URL however also content from the internet application. The proposed gadget can be evaluated using numerous available datasets.

Addressing the Credibility of Web Applications

2008 ◽  
pp. 23-28 ◽  
Author(s):  
Pankaj Kamthan
Keyword(s):  
Web Application ◽  
Web Applications ◽  
Consumer Confidence ◽  
Future Research ◽  
The Internet ◽  
Different Types ◽  
Set Up ◽  
The Web

The Internet, particularly the Web, has opened new vistas for businesses. The ability that anyone, using (virtually) any device could be reached anytime and anywhere presents a tremendous commercial prospective. In retrospect, the fact that almost anyone can set up a Web Application claiming to offer products and services raises the question of credibility from a consumers’ viewpoint. If not addressed, there is a potential for lost consumer confidence, thus significantly reducing the advantages and opportunities the Web as a medium offers. Establishing credibility is essential for an organization’s reputation (Gibson, 2002) and for building consumers’ trust (Kamthan, 1999). The rest of the article is organized as follows. We first provide the motivational background necessary for later discussion. This is followed by the introduction of a framework within which different types of credibility in the context of Web Applications can be systematically addressed and thereby improved. Next, challenges and directions for future research are outlined. Finally, concluding remarks are given.

scholarly journals A Tag And Social Network Based Recommender System

2021 ◽  
Author(s):  
Sogol Naseri
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
User Similarity ◽  
Nearest Neighbours ◽  
The Social ◽  
Recommendation Algorithms ◽  
Internet Information ◽  
Positive Results

In the era of the Internet, information overload is a growing problem which refers to the inability of a person to make a decision because the amount of information that she/he needs to process is huge. To solve this problem, recommender systems were proposed to apply various algorithms to recognize users’ preferences and generate recommendations which are likely match the user’s interest on various items. In this thesis, we aim to improve the effectiveness of the recommendation by incorporating the social data into the traditional recommendation algorithms. Hence, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendships and memberships, in measuring the nearest neighbours. Subsequently, we define a new recommendation method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on a Last.fm dataset show positive results of our proposed approach.

scholarly journals A Tag And Social Network Based Recommender System

2021 ◽  
Author(s):  
Sogol Naseri
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
User Similarity ◽  
Nearest Neighbours ◽  
The Social ◽  
Recommendation Algorithms ◽  
Internet Information ◽  
Positive Results

In the era of the Internet, information overload is a growing problem which refers to the inability of a person to make a decision because the amount of information that she/he needs to process is huge. To solve this problem, recommender systems were proposed to apply various algorithms to recognize users’ preferences and generate recommendations which are likely match the user’s interest on various items. In this thesis, we aim to improve the effectiveness of the recommendation by incorporating the social data into the traditional recommendation algorithms. Hence, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendships and memberships, in measuring the nearest neighbours. Subsequently, we define a new recommendation method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on a Last.fm dataset show positive results of our proposed approach.

Classification of Web Pages Using Machine Learning Techniques

2010 ◽  
pp. 134-150
Author(s):  
K. Selvakuberan ◽  
M. Indra Devi ◽  
R. Rajaram
Keyword(s):  
Machine Learning ◽  
Search Engines ◽  
Research Problem ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Web Pages ◽  
Current Research Problem ◽  
Learning Techniques ◽  
The Web

The explosive growth of the Web makes it a very useful information resource to all types of users. Today, everyone accesses the Internet for various purposes and retrieving the required information within the stipulated time is the major demand from users. Also, the Internet provides millions of Web pages for each and every search term. Getting interesting and required results from the Web becomes very difficult and turning the classification of Web pages into relevant categories is the current research topic. Web page classification is the current research problem that focuses on classifying the documents into different categories, which are used by search engines for producing the result. In this chapter we focus on different machine learning techniques and how Web pages can be classified using these machine learning techniques. The automatic classification of Web pages using machine learning techniques is the most efficient way used by search engines to provide accurate results to the users. Machine learning classifiers may also be trained to preserve the personal details from unauthenticated users and for privacy preserving data mining.

Classification of Web Pages Using Machine Learning Techniques

2012 ◽  
pp. 50-65 ◽  
Author(s):  
K. Selvakuberan ◽  
M. Indra Devi ◽  
R. Rajaram
Keyword(s):  
Machine Learning ◽  
Search Engines ◽  
Research Problem ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Web Pages ◽  
Current Research Problem ◽  
Learning Techniques ◽  
The Web

The explosive growth of the Web makes it a very useful information resource to all types of users. Today, everyone accesses the Internet for various purposes and retrieving the required information within the stipulated time is the major demand from users. Also, the Internet provides millions of Web pages for each and every search term. Getting interesting and required results from the Web becomes very difficult and turning the classification of Web pages into relevant categories is the current research topic. Web page classification is the current research problem that focuses on classifying the documents into different categories, which are used by search engines for producing the result. In this chapter we focus on different machine learning techniques and how Web pages can be classified using these machine learning techniques. The automatic classification of Web pages using machine learning techniques is the most efficient way used by search engines to provide accurate results to the users. Machine learning classifiers may also be trained to preserve the personal details from unauthenticated users and for privacy preserving data mining.

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