核心概念
The author meticulously categorizes recommendation system methodologies into four types and explores challenges and future research directions in the field. The paper also delves into real-world applications and societal impact of recommendation systems.
摘要
This comprehensive survey delves into the evolution, challenges, and applications of contemporary recommendation systems on big data. It categorizes methodologies, addresses future research directions, and highlights the impact of these systems across various domains.
The paper explores the classification of recommendation techniques into content-based, collaborative filtering, knowledge-based, and hybrid approaches. It discusses challenges like data sparsity and scalability while emphasizing the importance of diverse recommendations for user engagement.
Furthermore, it extends its analysis to real-world applications in marketing, healthcare, governance, and sustainable living. The potential of recommendation systems to shape digital experiences is highlighted along with their role in promoting sustainable lifestyles.
Overall, this survey provides a detailed overview of recommendation systems on big data, emphasizing their significance in enhancing user experiences and influencing societal trends through personalized recommendations.
統計資料
The paper meticulously categorizes the myriad of recommendation system methodologies into four principal types.
Challenges faced by recommendation systems include data sparsity, scalability issues.
Netflix's "Netflix Prize" challenge aimed to develop a recommender system surpassing their existing algorithm.
Hybrid-based recommendation systems combine advantages of multiple techniques to overcome weaknesses.
Big data platforms like Hadoop and Spark are widely used for big data architectures.
引述
"The survey underscores these challenges as promising directions for subsequent research endeavors within the discipline."
"These technologies play a crucial role in improving digital consumer culture."
"Recommendation systems have become widely integrated into various aspects of e-commerce operations."