News Recommender systems (NRSs) aid in decision-making in news media. However, undesired effects can emerge. Among these are selective exposures that may contribute to polarization, potentially reinforcing existing attitudes through belief perseverance—discounting contrary evidence due to their opposing attitudinal strength. This can be unsafe for people, making it difficult to accept information objectively. A crucial issue in news recommender system research is how to mitigate these undesired effects by designing recommender interfaces and machine learning models that enable people to consider to be more open to different perspectives. Alongside accurate models, the user experience is an equally important measure. Indeed, the core statistics are based on users’ behaviors and experiences in this research project. Therefore, this research agenda aims to steer the choices of readers’ based on altering their attitudes. The core methods plan to concentrate on the interface design and ML model building involving manipulations of cues, users’ behaviors prediction, NRSs algorithm and changing the nudges. In sum, the project aims to provide insight in the extent to which news recommender systems can be effective in mitigating polarized opinions.