Model-Based Book Recommender Systems using Naïve Bayes enhanced with Optimal Feature Selection
Author:
Affiliation:
1. School of Computer Science & Engineering, International University, VNU-HCMC, Ho Chi Minh City, Vietnam
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3316615.3316727
Reference11 articles.
1. C. C. Aggarwal. 2016. Recommender Systems. Springer International Publishing. C. C. Aggarwal. 2016. Recommender Systems. Springer International Publishing.
2. S. Yang M. Korayem K. AlJadda T. Grainger and S. Natarajan. 2017. Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach. Knowledge-Based Systems. 10.1016/j.knosys.2017.08.017 S. Yang M. Korayem K. AlJadda T. Grainger and S. Natarajan. 2017. Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach. Knowledge-Based Systems. 10.1016/j.knosys.2017.08.017
3. Deep Neural Networks for YouTube Recommendations
4. Learning Tree-based Deep Model for Recommender Systems
5. Introducing Hybrid Technique for Optimization of Book Recommender System
Cited by 9 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. Study of Machine Learning for Recommendation Systems;Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications;2023-08-14
2. Transforming Data with Ontology and Word Embedding for an Efficient Classification Framework;EAI Endorsed Transactions on Industrial Networks and Intelligent Systems;2023-06-01
3. Reinforced PU-learning with Hybrid Negative Sampling Strategies for Recommendation;ACM Transactions on Intelligent Systems and Technology;2023-05-08
4. Comparative Study of Pattern Recognition Techniques in the Classification of Vertebral Column Diseases;Communications in Computer and Information Science;2023
5. An Empirical Investigation of Personalized Recommendation and Reward Effect on Customer Behavior: A Stimulus–Organism–Response (SOR) Model Perspective;Sustainability;2022-11-18
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