Enhancing Citizens' Participation via Recommender Systems

2020 ◽  
pp. 624-650
Author(s):  
Luis Terán
Keyword(s):  
Decision Making ◽  
Semantic Web ◽  
Private Sector ◽  
Web 2.0 ◽  
Recommender Systems ◽  
Search Engines ◽  
Information Overload ◽  
Relevant Information ◽  
Research Topic ◽  
Government Services

With the introduction of Web 2.0, which includes users as content generators, finding relevant information is even more complex. To tackle this problem of information overload, a number of different techniques have been introduced, including search engines, Semantic Web, and recommender systems, among others. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. In this chapter, the use of recommender systems on eParticipation is presented. A brief description of the eGovernment Framework used and the participation levels that are proposed to enhance participation. The highest level of participation is known as eEmpowerment, where the decision-making is placed on the side of citizens. Finally, a set of examples for the different eParticipation types is presented to illustrate the use of recommender systems.

Enhancing Citizens' Participation via Recommender Systems

2015 ◽  
pp. 99-123
Author(s):  
Luis Terán
Keyword(s):  
Decision Making ◽  
Semantic Web ◽  
Private Sector ◽  
Web 2.0 ◽  
Recommender Systems ◽  
Search Engines ◽  
Information Overload ◽  
Relevant Information ◽  
Research Topic ◽  
Government Services

With the introduction of Web 2.0, which includes users as content generators, finding relevant information is even more complex. To tackle this problem of information overload, a number of different techniques have been introduced, including search engines, Semantic Web, and recommender systems, among others. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. In this chapter, the use of recommender systems on eParticipation is presented. A brief description of the eGovernment Framework used and the participation levels that are proposed to enhance participation. The highest level of participation is known as eEmpowerment, where the decision-making is placed on the side of citizens. Finally, a set of examples for the different eParticipation types is presented to illustrate the use of recommender systems.

scholarly journals Crowdsourced Recommender System

2018 ◽  
Author(s):  
Mario Mallia Milanes ◽  
Matthew Montebello
Keyword(s):  
Web 2.0 ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Relevant Information ◽  
Learning Systems ◽  
Learning Material ◽  
Timely Manner ◽  
Online Learners ◽  
E Learning

The use of artificially intelligent techniques to overcome specific shortcomings within e-learning systems is a well-researched area that keeps on evolving in an attempt to optimise such resourceful practices. The lack of personalization and the sentiment of isolation coupled with a feeling of being treated like all others, tends to discourage and push learners away from courses that are very well prepared academically and excellently projected intellectually. The use of recommender systems to deliver relevant information in a timely manner that is specifically differentiated to a unique learner is once more being investigated to alievate the e-learning issue of being impersonal.  The application of such a technique also assists the learner by reducing information overload and providing learning material that can be shared, criticized and reviewed at one’s own pace. In this paper we propose the use of a fully automated recommender system based on recent AI developments together with Web 2.0 applications and socially networked technologies. We argue that such technologies have provided the extra capabilities that were required to deliver a realistic and practical interfacing medium to assist online learners and take recommender systems to the next level.

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

2012 ◽  
pp. 684-705 ◽  
Author(s):  
Luis Terán ◽  
Andreas Ladner ◽  
Jan Fivaz ◽  
Stefani Gerber
Keyword(s):  
Recommender Systems ◽  
Information And Communication Technologies ◽  
Information Overload ◽  
Relevant Information ◽  
The Internet ◽  
Clustering Methods ◽  
Web Based ◽  
Information And Communication ◽  
Fuzzy Clustering Methods ◽  
Use Of The Internet

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 e-Commerce to suggest items that fit a customer’s purchasing tendencies. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. More specifically, e-Democracy 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 e-Elections 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.

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

2012 ◽  
pp. 115-138 ◽  
Author(s):  
Luis Terán ◽  
Andreas Ladner ◽  
Jan Fivaz ◽  
Stefani Gerber
Keyword(s):  
Recommender Systems ◽  
Information And Communication Technologies ◽  
Information Overload ◽  
Relevant Information ◽  
The Internet ◽  
Clustering Methods ◽  
Web Based ◽  
Information And Communication ◽  
Fuzzy Clustering Methods ◽  
Use Of The Internet

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 e-Commerce to suggest items that fit a customer’s purchasing tendencies. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. More specifically, e-Democracy 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 e-Elections 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.

Who owns and cares about the data? A method for identifying and gathering information for business research investigations

2021 ◽  
pp. 026638212098472
Author(s):  
Ann Cullen ◽  
Patrick S Noonan
Keyword(s):  
Decision Making ◽  
Business School ◽  
Information Overload ◽  
Relevant Information ◽  
Search Algorithms ◽  
Project Work ◽  
Business Research ◽  
Business Decision ◽  
Pertinent Information ◽  
Present Part

Information overload has always been a challenge for businesspeople as well as professionals from other types of organizations. And today with search algorithms and artificial intelligence (AI) such an ever-present part of daily life and media consumption, the challenges in learning how to filter information for oneself for effective processing, interpretation and analysis have only increased. This article presents several frameworks that were created for instructing students to assist with addressing this. They were tested and refined over four years in a core MBA course focused on decision making and project-based work. They include ways to conceptualize the broad areas of information available for business decision making as well as how to identify information by thinking about who is producing it, why they are producing it and who their key customers are. Other frameworks presented deal with ways to identify pertinent information and how to process and work with it as part of a research investigation. These frameworks are presented as tools that can be used by business school instructors, but certainly have a broader application as useful guidelines for anyone hoping to be a better collector and processor of relevant information for decision making and project work.

scholarly journals User-Query Processing through Dynamic Tweets Status Recommender System

2021 ◽  
Vol 58 (1) ◽  
pp. 5600-5606
Author(s):  
V. Kakulapati, D. Vasumathi, G. Suryanarayana
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
User Interfaces ◽  
Online Social Networks ◽  
Information Overload ◽  
Relevant Information ◽  
Twitter User ◽  
User Query ◽  
Text Filtering ◽  
Twitter Users

With increasing user information volume in online social networks, recommender systems have been an effective method to limit such information overload. The requirements of recommender systems specified, with widespread adoption in many internet social Twitter, Facebook, and Google online applications. In recent years,  the  micro-blogging  in  Twitter  has  brought  greater  importance  to  online  users  as  a  channel  spreading knowledge  and  information.  Through  Twitter,  users  can  find  the  relevant  information  on  the  search  they perform,  but  understanding  the  past,  present,  and  future  information  relevant  to  the  investigation  source  is needed real-time information. Estimating the successful tweet status (history, ongoing, and prospective) among the huge population of Twitter members is important to satisfy the needs of Twitter online content readers. In this paper, a Dynamic Tweets Status Recommender System (DTSRS) is designed by creating a set of dynamic recommendations to a Twitter user based on usability, consisting of people who post tweets, which is exciting present and future. The proposed recommender system is implemented through two approaches: the first is to analyze  the  Twitter  member  online  tweets,  select  and  understand  the  content  of  that  tweet,  and  the  second predicts  the  understanding  of  the  tweet  content,  suggest  the  dynamic  status  of  the  tweets.  In  this  paper,  the Twitter user tweets' views are expressed after examining the depth of content, different types of user interfaces, text filtering, and machine learning technique. The set of results through tweets experimentations with database operators carried out to evaluate and comparability the proposed recommender system's performance.  

scholarly journals Semantic Web Search Engines : A Comparative Survey

2019 ◽  
pp. 107-115
Author(s):  
Anita Kumari ◽  
Jawahar Thakur
Keyword(s):  
Semantic Web ◽  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Retrieval System ◽  
Relevant Information ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Web Search Engine ◽  
Comparative Survey

Search engines play important role in the success of the Web. Search engine helps the users to find the relevant information on the internet. Due to many problems in traditional search engines has led to the development of semantic web. Semantic web technologies are playing a crucial role in enhancing traditional search, as it work to create machines readable data and focus on metadata. However, it will not replace traditional search engines. In the environment of semantic web, search engine should be more useful and efficient for searching the relevant web information. It is a way to increase the accuracy of information retrieval system. This is possible because semantic web uses software agents; these agents collect the information, perform relevant transactions and interact with physical devices. This paper includes the survey on the prevalent Semantic Search Engines based on their advantages, working and disadvantages and presents a comparative study based on techniques, type of results, crawling, and indexing.

scholarly journals Do You Buy or Not? The Effect of Recommender Slogans on Retail Sites

2021 ◽  
Vol 11 (1) ◽  
pp. 11-20
Author(s):  
Hsiaoping Yeh ◽  
◽  
Tsung-Sheng Chang ◽  
Fenghung Kuo
Keyword(s):  
Recommender Systems ◽  
Information Overload ◽  
Relevant Information ◽  
Purchase Decisions ◽  
Price Promotion ◽  
Product Categories ◽  
Current Information ◽  
Online Retailers ◽  
Online Shoppers ◽  
Retail Shopping

Recommender systems solve the current information overload problem in the online world. By predicting and presenting relevant information, web users do not need to waste time searching and browsing for contents that they are interested in. However, in addition to the accurate contents, slogans associated catching the customers’ eyes are worthy of exploration. This study aims to discover the effects of various recommender slogans. Two categories of slogans were designed in the study: slogans associated with customer inputs and slogans associated with price promotion. Actual customers’ webpage clickstreams and purchase decisions were collected from a Taiwanese retail shopping Website. The effects of recommender slogans on product categories are different. Customers generally were drawn by the slogans associated with price promotion. This study brought to light the effects of different slogans on online shoppers. With the empirical findings, this study provides online retailers important guidelines regarding online customers’ behaviors towards the employment of recommender slogans.

Semantic Web Technologies in the Service of Personalization Tools

2010 ◽  
pp. 68-87 ◽  
Author(s):  
Yolanda Blanco-Fernández ◽  
José J. Pazos-Arias ◽  
Alberto Gil-Solla
Keyword(s):  
Semantic Web ◽  
Recommender Systems ◽  
Information Overload ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Manual Search ◽  
Novel Approach ◽  
Search Processes ◽  
Matching Techniques

The so-called recommender systems have become assistance tools indispensable to the users in domains where the information overload hampers manual search processes. In literature, diverse personalization paradigms have been proposed to match automatically the preferences of each user (which are previously modelled in personal profiles) against the available items. All these paradigms are laid down on a common substratum that uses syntactic matching techniques, which greatly limit the quality of the offered recommendations due to their inflexible nature. To fight these limitations, this chapter explores a novel approach based on reasoning about the semantics of both the users’ preferences and considered items, by resorting to less rigid inference mechanisms borrowed from the Semantic Web.

scholarly journals Implementing Recommender Systems using Machine Learning and Knowledge Discovery Tools

2021 ◽  
Vol 2 (2) ◽  
pp. 44-53
Author(s):  
Mohammad Zahrawi ◽  
Ahmad Mohammad
Keyword(s):  
Machine Learning ◽  
Knowledge Discovery ◽  
Recommender Systems ◽  
Learning Strategies ◽  
Search Engines ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Relevant Information ◽  
Different Characteristics ◽  
Better Than

The current research offers a novel use of machine learning strategies to create a recommendation system. At recent era, recommender systems (RSs) have been used widely in e-commerce, entertainment purposes, and search engines. In more general, RSs are set of algorithms designed to recommend relevant items to users (movies to watch, books to read, products to buy, songs to listen, and others). This article discovers the different characteristics and features of many approaches used for recommendation systems in order to filter and prioritize the relevant information and work as a compass for searching. Recommender engines are crucial in some companies as they can create a big amount of income when they are effective or be a way to stand out remarkably from other rivals. As a proof of the importance of recommender engine, it can be stated that Netflix arrange a challenge (the “Netflix prize”) where the mission was to create a recommender engine that achieves better than its own algorithm with a prize of 1 million dollars to win.

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