Abstract
Traditional personalized news recommendation methods still face several limitations, such as inadequate modeling of dynamic user interests, difficulty in balancing accuracy and diversity, and significant performance degradation in cold-start scenarios. These limitations hinder their effectiveness in real-world applications. To address these issues, a Personalized News Recommendation Model via a Fully Connected Neural Network (MT-FCNN) is proposed. The model utilizes user behavior sequence embeddings to capture the dynamic evolution of user interests and adopts a multi-task learning framework that jointly optimizes click intention prediction and interest distribution learning, thereby improving recommendation performance in a unified representation space. The proposed model is evaluated on the publicly available Microsoft News Dataset and compared with established approaches, including Collaborative Filtering, Content-Based Recommendation, GRU4Rec, and Transformer-based models. Experimental results show that MT-FCNN improves AUC, NDCG@5, and CTR by 12.4%, 10.7%, and 8.9%, respectively. For cold-start users, recommendation accuracy (Precision@5) improves by 11.2%. Repeated experiments and statistical tests further confirm the model's stability and significant advantages. This integrated framework introduces a new methodological perspective for recommendation systems while offering a more efficient technological approach for modeling user behavior in dynamic environments.