With the rapid development of the Internet, people can browse and read a massive amount of information online. Although the information is diverse and abundant, the sheer volume, coupled with excessive duplication or similarity, can easily leave users feeling overwhelmed. In addition, users often spend a great deal of time searching for content they are genuinely interested in. Recommender systems were thus created to address this problem. This research aims to develop a news recommendation technology that features news data filtering and extraction, personalized recommendations, and rapid progressive updates. Based on generalized additive mixed effect model (GAME), the personalized news recommendation model, which comprises a fixed-effect model and many random-effect models, is designed in this paper. The recommendation results from these two types of models are then combined according to weights to produce the final recommendation. Based on the model trained offline, incremental learning is incorporated to update news data and user viewing content in real-time, resulting in the development of a fast, progressive personalized news recommendation technology. Experiments were also conducted to show the performance of the proposed model.