scholarly journals Association-Rules-Based Recommender System for Personalization in Adaptive Web-Based Applications

2010 ◽  
pp. 85-90 ◽  
Author(s):  
Daniel Mican ◽  
Nicolae Tomai
Keyword(s):  
Association Rules ◽  
Recommender System ◽  
Web Based

WEBLORS – A Personalized Web-Based Recommender System

2019 ◽  
pp. 258-266
Author(s):  
Mohammad Belghis-Zadeh ◽  
Hazra Imran ◽  
Maiga Chang ◽  
Sabine Graf
Keyword(s):  
Recommender System ◽  
Web Based

DataPro

2021 ◽  
pp. 98-108
Author(s):  
Vala Ali Rohani ◽  
Sedigheh Moghavvemi ◽  
Tiago Pinho ◽  
Paulo Caldas
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Unemployment Rate ◽  
Recommender System ◽  
Social Problems ◽  
National Level ◽  
Point Of View ◽  
Web Based ◽  
Four Dimensions ◽  
Online Portal

Due to the COVID‐19 pandemic, most countries are exposed to unprecedented social problems in the current global situation. According to the official reports, it caused a dramatic increase of 44% in graduates' unemployment rate in Portugal. Moreover, from the human resource point of view, the whole of Europe is expected to face a shortage of 925,000 data professionals by 2025. Given the existing situations, the DataPro aims to propose a national-level reskilling solution in big data to mitigate both social problems of unemployability and the shortage of data professionals in Portugal. DataPro project consists of four dimensions, including an online portal for the hiring companies and unemployed graduates, along with a web-based analytics talent upskilling (ATU) platform empowered by an artificial intelligence recommender system to match the reskilled data professionals and the hiring companies.

scholarly journals Data Mining as Support to Knowledge Management in Marketing

2015 ◽  
Vol 6 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Adela Has
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Management ◽  
Association Rules ◽  
Marketing Strategies ◽  
Web Based ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
Model Generalization ◽  
Retail Managers

Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability.

Corrigendum to “Recommender system based on pairwise association rules” [Expert Systems With Applications 115 (2018) 535–542]

2019 ◽  
Vol 135 ◽  
pp. 410
Author(s):  
Timur Osadchiy ◽  
Ivan Poliakov ◽  
Patrick Olivier ◽  
Maisie Rowland ◽  
Emma Foster
Keyword(s):  
Expert Systems ◽  
Association Rules ◽  
Recommender System
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