News Recommendation

News recommendation systems aim to personalize news feeds by predicting user preferences and delivering relevant articles. Current research emphasizes improving recommendation accuracy and diversity through advanced techniques like large language models (LLMs), graph neural networks, and ensemble methods, often incorporating user behavior data (clicks, dwell time) and contextual information (news category, popularity). This field is significant due to its impact on information access and the potential for mitigating filter bubbles and promoting more informed public discourse, driving ongoing efforts to balance personalization with editorial values and responsible information dissemination.