Contemporary Recommendation Systems on Big Data and Their Applications: A Survey

被引：0
作者：
Xia, Ziyuan [1 ]
Sun, Anchen [2 ]
Xu, Jingyi [3 ,4 ]
Peng, Yuanzhe [5 ]
Ma, Rui [6 ]
Cheng, Minghui [7 ,8 ]
机构：
[1] Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Shanghai 200030, Peoples R China
[2] Univ Miami, Dept Elect & Comp Engn, Coral Gables, FL 33146 USA
[3] Cornell Univ, Dept Architecture, Ithaca, NY 14853 USA
[4] HOKs Miami Studio, Coral Gables, FL 33134 USA
[5] Univ Florida, Dept Elect & Comp Engn, Gainesville, FL 32611 USA
[6] Univ Miami, Bascom Palmer Eye Inst, Miller Sch Med, Miami, FL 33136 USA
[7] Univ Miami, Dept Civil & Architectural Engn, Coral Gables, FL 33146 USA
[8] Univ Miami, Sch Architecture, Coral Gables, FL 33146 USA
来源：
IEEE ACCESS | 2024年 / 12卷
关键词：
Recommender systems; Big Data; Knowledge based systems; Scalability; Collaboration; Data privacy; Sustainable development; Surveys; Reviews; Prediction algorithms; Recommendation system; big data; machine learning; sustainability; ENERGY EFFICIENCY; TRANSMISSION; SIMULATION; CHALLENGES; STATE;
D O I：
10.1109/ACCESS.2024.3517492
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This survey paper provides a comprehensive analysis of the evolution and current landscape of recommendation systems, extensively used across various web applications. It categorizes recommendation techniques into four main types: content-based, collaborative filtering, knowledge-based, and hybrid approaches, tailored for specific user contexts. The review spans historical developments to cutting-edge innovations, with a focus on big data analytics applications, state-of-the-art recommendation models, and evaluation using prominent datasets like MovieLens, Amazon Reviews, Netflix Prize, Last.fm, and Yelp. The paper addresses significant challenges such as data sparsity, scalability, and the need for diverse recommendations, highlighting these as key directions for future research. It also explores practical applications and the integration challenges of recommendation systems in everyday life, underscoring the potential of big data-driven advancements to significantly enhance real-world experiences.
引用
收藏
页码：196914 / 196928
页数：15
相关论文
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