Intelligent e-government services with personalized recommendation techniques

Guo, Xuetao; Lu, Jie

doi:10.1002/int.20206

Cited by 93 publications

(90 citation statements)

References 28 publications

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“…Recommendations based on fuzzy relations are used to reflect the graded or uncertain information in the G2B techniques (Ghazanfar & Prügel-Bennett, 2014;Guo & Lu, 2007).…”

Section: State Of the Literaturementioning

confidence: 99%

E-Assessment and Computer-Aided Prediction Methodology for Student Admission Test Score

Usman

Iqbal

et al. 2017

EURASIA J MATH SCI T

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Machine Learning is a scientific discipline that addresses learning in context is not learning by heart but recognizing complex patterns and makes intelligent decisions based on data. Currently, students have to face the problem of selecting the best suitable university for admission in engineering. There is no predictor system that recommends the students to select the specific category which is best to its academic career. Students have to first appear in the entry test and can't predict whether he/she can pass the entry test to get admitted in University. To tackle this problem the field of Machine Learning develops algorithms that discover knowledge from specific data and experience, based on sound statistical and computational principles. After going through the entry test students have to face problems for selecting the preferences among different categories due to the lack of knowledge of intake merits of preceding years. Another problem arises when students are waiting for admission in specific university, meanwhile, other universities finish their admission processes and select the students, but some students can't take admission in any university due to no prediction system for admission in universities. In this work, we would like to develop an E-Assessment and Computer-Aided Prediction online system that enables the student to predict the entry test numbers by giving the Metric and Intermediate marks and other academic numbers. The suggested scheme has been demonstrated to perform at the maximum speed under MATLAB setup.

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“…Recommendations based on fuzzy relations are used to reflect the graded or uncertain information in the G2B techniques (Ghazanfar & Prügel-Bennett, 2014;Guo & Lu, 2007).…”

Section: State Of the Literaturementioning

confidence: 99%

E-Assessment and Computer-Aided Prediction Methodology for Student Admission Test Score

Usman

Iqbal

et al. 2017

EURASIA J MATH SCI T

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“…The most-used techniques in recommender systems are based on collaborative filtering technologies according to Guo et al (2007) and Sarwar et al (2001). They include collaborative filtering algorithms that are memory-based (i.e., user-based) and model-based (i.e., item-based).…”

Section: Motivationmentioning

confidence: 99%

“…These types of recommender systems are suitable for the one-and-only item, according to Guo et al (2007), where the recommendation target is a unique item/event. Examples of one-and-only items include the sale of a house, trade exhibitions, elections, voting, and community building efforts, among others, where recommendations make no use of past actions since these events occurred only once.…”

Section: Fig 1 Egovernment Frameworkmentioning

confidence: 99%

Using a Fuzzy-Based Cluster Algorithm for Recommending Candidates in E-Elections

Terán

Ladner²,

Fivaz

et al. 2012

Digital Democracy

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The use of the Internet now has a specific purpose: to find information. Unfortunately, the amount of data available on the Internet is growing exponentially, creating what can be considered a nearly infinite and ever-evolving network with no discernable structure. This rapid growth has raised the question of how to find the most relevant information.Many different techniques have been introduced to address the information overload, including search engines, semantic web, and recommender systems, among others.Recommender systems are computer-based techniques that are used to reduce information overload and recommend products likely to interest a user when given some information about the user's profile. This technique is mainly used in eCommerce to suggest items that fit a customer's purchasing tendencies.The use of recommender systems for eGovernment is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on eGovernment services. More specifically, eDemocracy aims to increase citizens' participation in democratic processes through the use of information and communication technologies.In this chapter, an architecture of a recommender system that uses fuzzy clustering methods for eElections is introduced. In addition, a comparison with the smartvote system, a Web-based Voting Assistance Application (VAA) used to aid voters in finding the party or candidate that is most in line with their preferences, is presented.

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“…It can be further divided into user-based and item- Figure 1. Two examples of telecom service package based CF approaches [8]. User-based CF first finds a set of nearest neighbors of a target user by computing correlations or similarities between users.…”

Section: Related Workmentioning

confidence: 99%

“…A semantic match with the domain ontology is used to find similar users. A semantic product relevance model is integrated into the traditional item-based CF recommender system in [8] to recommend the one-and-only items, such as trade exhibitions. In [4], they integrate fuzzy sets based semantic similarity and traditional item-based CF methods to improve recommendation accuracy.…”

Section: Related Workmentioning

confidence: 99%

A hybrid recommendation approach for hierarchical items

Zhang

2010

2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering

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Abstract-Recommender systems aim to recommend items that are likely to be of interest to the user. In many business situations, complex items are described by hierarchical tree structures, which contain rich semantic information. To recommend hierarchical items accurately, the semantic information of the hierarchical tree structures must be considered comprehensively. In this study, a new hybrid recommendation approach for complex hierarchical tree structured items is proposed. In this approach, a comprehensive semantic similarity measure model for hierarchical tree structured items is developed. It is integrated with the traditional item-based collaborative filtering approach to generate recommendations.

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Intelligent e-government services with personalized recommendation techniques

Cited by 93 publications

References 28 publications

E-Assessment and Computer-Aided Prediction Methodology for Student Admission Test Score

E-Assessment and Computer-Aided Prediction Methodology for Student Admission Test Score

Using a Fuzzy-Based Cluster Algorithm for Recommending Candidates in E-Elections

A hybrid recommendation approach for hierarchical items

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