Recommendation System using Hybrid Fuzzy Association Rules for Human Smart Cities

2018 ◽  
Author(s):  
Nicola Convertini ◽  
Nicola Logrillo ◽  
Fabio Manca ◽  
Tonino Palmisano
Keyword(s):  
Association Rules ◽  
Recommendation System ◽  
Smart Cities ◽  
Fuzzy Association Rules

Parallel mining and application of fuzzy association rules

2006 ◽  
Vol 1 (2) ◽  
pp. 177-182
Author(s):  
Jian-jiang Lu ◽  
Bao-wen Xu ◽  
Xiao-feng Zou ◽  
Da-zhou Kang ◽  
Yan-hui Li ◽  
...  
Keyword(s):  
Association Rules ◽  
Fuzzy Association Rules ◽  
Parallel Mining

scholarly journals Mining Fuzzy Association Rules in the Framework of AFS Theory

2015 ◽  
Vol 2 (3) ◽  
pp. 261-270 ◽  
Author(s):  
Bo Wang ◽  
Xiao-dong Liu ◽  
Li-dong Wang
Keyword(s):  
Association Rules ◽  
Fuzzy Association Rules ◽  
Afs Theory

scholarly journals Phone-Based Restaurant Recommendation System by Using Position Information and Association Rules

2012 ◽  
Vol 6-7 ◽  
pp. 783-789
Author(s):  
Jian Feng Dong ◽  
Tian Yang Dong ◽  
Jia Jie Yao ◽  
Ling Zhang
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Association Rules ◽  
Recommendation System ◽  
Rapid Development ◽  
Smart Phone ◽  
Smart Phones ◽  
Location Information ◽  
Position Information ◽  
The Real

With the rapid development of smart-phone applications, how to make the ordering process via smart-phones more convenient and intelligent has become a hotspot. This paper puts forward a method of restaurant dish recommendation relying on position information and association rules. In addition, this paper has designed and developed a restaurant recommendation system based on mobile phone. The system would fetch the real-time location information via smart-phones, and provide customers personalized restaurant and dish recommendation service. According to the related applications, this system can successfully recommend the related restaurants and food information to customers.

scholarly journals A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 177-191
Author(s):  
Theodoros Anagnostopoulos
Keyword(s):  
Recommendation System ◽  
Smart Cities ◽  
Urban Pollution ◽  
Real Data ◽  
Movement Behavior ◽  
Daily Movement ◽  
Passenger Transportation ◽  
Ride Sharing ◽  
Mobility Behavior ◽  
User Movement

Smart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, transport commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement schedule. The use of a private vehicle per single passenger transportation is no longer viable in sustainable Smart Cities (SC) because of the vehicles’ resource allocation and urban pollution. The current research on car ride sharing systems is widely expanding in a range of contemporary technologies, however, without covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well as a recommender system based on riders’ personalized information. Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem. The adopted system also provides a recommendation to citizens to select the persons they would like to commute with. An Artificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list. An experimented is conducted with real data from the municipality of New Philadelphia, in SC of Athens, Greece, to implement the proposed system and observe certain user movement behavior. The results are promising for the incorporation of the adopted system to other SCs.

scholarly journals Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

2015 ◽  
Vol 67 (1) ◽  
pp. 99-104 ◽  
Author(s):  
Gabroveanu Mihai
Keyword(s):  
Association Rules ◽  
Management System ◽  
Recommendation System ◽  
Learning Management System ◽  
Distributed Learning ◽  
Learning Management Systems ◽  
Management Systems ◽  
User Data ◽  
E Learning ◽  
Learning Management

Abstract Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

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