# Recent Developments in Recommender Systems: A Survey

**Authors:** Yang Li, Kangbo Liu, Ranjan Satapathy, Suhang Wang, Erik Cambria  
**arXiv:** [2306.12680](https://arxiv.org/abs/2306.12680)  
**Date:** 2023-06-22  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2306.12680v1/

## Key points

- The technical survey titled "Recent Developments in Recommender Systems: A Survey" provides a comprehensive overview of the latest advancements in the field of recommender systems.
- The study is authored by Yang Li, Kangbo Liu, Ranjan Satapathy, Suhang Wang, and Erik Cambria.
- The objective of this survey is to present an up-to-date summary of the current state-of-the-art in the field and highlight the latest trends in recommender system development.
- The study begins with a detailed taxonomy of recommender systems that includes personalized and group-based systems, followed by an exploration into knowledge-based recommender systems.
- The authors analyze the robustness, data bias, and fairness issues associated with these systems while summarizing evaluation metrics used to assess their performance.
- Furthermore, the authors provide insights into recent trends in recommender system development and highlight new directions for future research.
- This survey serves as a valuable resource for researchers and practitioners interested in understanding the latest advancements in recommender system technology.

## Summary

The technical survey titled "Recent Developments in Recommender Systems: A Survey" provides a comprehensive overview of the latest advancements in the field of recommender systems. The study is authored by Yang Li, Kangbo Liu, Ranjan Satapathy, Suhang Wang, and Erik Cambria. The objective of this survey is to present an up-to-date summary of the current state-of-the-art in the field and highlight the latest trends in recommender system development. The study begins with a detailed taxonomy of recommender systems that includes personalized and group-based systems, followed by an exploration into knowledge-based recommender systems. The authors then analyze the robustness, data bias, and fairness issues associated with these systems while summarizing evaluation metrics used to assess their performance. Furthermore, the authors provide insights into recent trends in recommender system development and highlight new directions for future research. This survey serves as a valuable resource for researchers and practitioners interested in understanding the latest advancements in recommender system technology. Overall, this study provides a comprehensive summary that will be useful for anyone seeking to stay up-to-date on this rapidly evolving field. It offers an insightful overview of recent developments in recommender systems that can help readers gain a better understanding of this complex topic.