A Bibliometric Perspective on Research of Personalized Recommendation Systems and the Filter Bubble Effect in 2024

This research adopts a bibliometric approach to investigate the topic of personalized recommendation systems and the information cocoon effect in 2024. Through a systematic review of literature from specific data sources and the application of diverse analytical methods, it provides a detailed examination of the current state and developmental trends in this field. The analysis encompasses the distribution of core authors, prominent journals, and leading countries, highlighting the main research forces in this domain. Co-occurrence and clustering analyses of keywords offer precise insights into research hotspots and emerging directions. This study not only synthesizes the existing research landscape but also identifies pressing challenges and proposes future directions. It aims to offer a comprehensive and detailed reference to support the sustainable development of this field, promote the optimization of personalized recommendation systems, alleviate the information cocoon effect, and contribute to the creation of a healthy, harmonious, and open information society. The findings are of substantial theoretical and practical significance.