A multi-task learning-based fully connected neural network for personalized news recommendation

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.