A contrastive news recommendation framework based on curriculum learning: A contrastive news recommendation

News recommendation is an intelligent technology that aims to provide users with matching news content based on their preferences and interests. Nevertheless, current methodologies exhibit significant limitations. Traditional models often rely on simple random negative sampling for training, an approach that insufficiently captures the patterns and preferences of users’ clicking behavior, thereby undermining the model’s effectiveness. Furthermore, these systems often face challenges in insufficient modeling due to the limited nature of user interactions. Considering these challenges, this paper presents a c ontrastive n ews r ecommendation framework based on c urriculum l earning (CNRCL). Specifically, we relate the negative sampling process to users’ interests and employ curriculum learning to guide the negative sampling procedure. To address the issue of insufficient user interest modeling, we propose to use contrastive learning to bring the user closer to news that is similar to the candidate news, thus enhancing the model’s accuracy in predicting user interests, and compensating for limited click behavior. Extensive experimental results on the MIND dataset verify the effectiveness of the model and improve the performance of news recommendation. Our code can be obtained from https://github.com/IIP-Lab-2024/CNRCL .