Optimizing News Recommendation System through Algorithms and Artificial Intelligence: Methods and Evaluation to Improve Accuracy and Diversity

Abstract

This research paper explores the potential of algorithms and artificial intelligence in optimizing news recommendation systems to improve accuracy and diversity. The importance of accuracy and diversity in news recommendation systems is discussed in the context of the news industry. A literature review examines the evolution of news recommendation systems, the impact of algorithms and artifi cial intelligence on the news industry, theoretical models related to accuracy, diversity, and personalization, and the challenges and limitations of existing news recommendation systems. The methodology section describes the algorithms and artificial intelligence techniques used in the study and the research method and evaluation criteria. An empirical study is conducted on a dataset of news articles and user behavior collected from a news website. The study compares the performance of diff erent algorithms and artifi cial intelligence techniques in improving the accuracy and diversity of news recommendation system. The results show that the hybrid model combining collaborative fi ltering and content-based fi ltering achieves a good balance between accuracy and diversity and generates more personalized and relevant news recommendations. The study concludes with a discussion of the practical signifi cance and prospects of optimizing news recommendation system through algorithms and artifi cial intelligence and the limitations and future research directions.(This thesis was funded by the China National Study Abroad Fund)