A multi-task learning-based fully connected neural network for personalized news recommendation (opens in new tab)

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 emb...