# A Survey on Modern Recommendation System based on Big Data

**Authors:** Yuanzhe Peng  
**arXiv:** [2206.02631](https://arxiv.org/abs/2206.02631)  
**Date:** 2022-05-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2206.02631v1/

## Key points

- Recommendation systems have gained immense popularity in recent years and are widely used in various web applications.
- These systems aim to provide personalized recommendations of online products or services to users.
- Various recommendation techniques such as content-based, collaborative filtering-based, knowledge-based, and hybrid-based recommendation systems have been developed to fulfill the needs of different scenarios.
- "A Survey on Modern Recommendation System based on Big Data" by Yuanzhe Peng presents a comprehensive review of historical and recent state-of-the-art recommendation approaches.
- The paper also delves into an in-depth analysis of groundbreaking advances in modern recommendation systems based on big data.
- Challenges faced by modern recommendation systems include sparsity, scalability, and diversity which can be transformed into prolific future research avenues.
- This survey provides valuable insights into modern recommendation systems based on big data and serves as a helpful resource for researchers and practitioners working in this field.

## Summary

In recent years, recommendation systems have gained immense popularity and are widely used in various web applications. These systems aim to provide personalized recommendations of online products or services to users. To fulfill the needs of different scenarios, various recommendation techniques such as content-based, collaborative filtering-based, knowledge-based, and hybrid-based recommendation systems have been developed. In this paper titled "A Survey on Modern Recommendation System based on Big Data" by Yuanzhe Peng, a comprehensive review of historical and recent state-of-the-art recommendation approaches is presented. The paper also delves into an in-depth analysis of groundbreaking advances in modern recommendation systems based on big data. Furthermore, the paper sheds light on the challenges faced by modern recommendation systems such as sparsity, scalability, and diversity. It illustrates how these challenges can be transformed into prolific future research avenues. Overall, this survey provides valuable insights into modern recommendation systems based on big data and serves as a helpful resource for researchers and practitioners working in this field.