AKNOBAS: A knowledge-based segmentation recommender system based on intelligent data mining techniques

Author:

Rodríguez-González Alejandro1,Torres-Niño Javier1,Jimenez-Domingo Enrique1,Gomez-Berbis Miguel1,Alor-Hernandez Giner2

Affiliation:

1. Computer Science Department, University Carlos III of Madrid, Leganes, Madrid, Spain

2. División de Estudios de Postgrado e Investigación, Instituto Tecnológico de Orizaba, Orizaba, Veracruz, México

Abstract

Recommender Systems have recently undergone an unwavering improvement in terms of efficiency and pervasiveness. They have become a source of competitive advantage in many companies which thrive on them as the technological core of their business model. In recent years, we have made substantial progress in those Recommender Systems outperforming the accuracy and added-value of their predecessors, by using cutting-edge techniques such as Data Mining and Segmentation. In this paper, we present AKNOBAS, a Knowledge-based Segmentation Recommender System, which follows that trend using Intelligent Clustering Techniques for Information Systems. The contribution of this Recommender System has been validated through a business scenario implementation proof-of-concept and provides a clear breakthrough of marshaling information through AI techniques.

Publisher

National Library of Serbia

Subject

General Computer Science

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Review on Stock Market Analysis Using Association Rule Mining;Cybernetics, Cognition and Machine Learning Applications;2022-09-16

2. Improving Collaborative Filtering’s Rating Prediction Quality by Exploiting the Item Adoption Eagerness Information;IEEE/WIC/ACM International Conference on Web Intelligence;2019-10-14

3. Knowledge-Based Leisure Time Recommendations in Social Networks;Current Trends on Knowledge-Based Systems;2017

4. SMORE: Towards a semantic modeling for knowledge representation on social media;Science of Computer Programming;2016-06

5. Ontology-Based Music Recommender System;Distributed Computing and Artificial Intelligence, 12th International Conference;2015

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3