Enhancing News Recommendation with Real-Time Feedback and Generative Sequence Modeling
Qi Zhang, Jieming Zhu, Jiansheng Sun, Guohao Cai, Ruining Yu, Bangzheng He, Liangbi Li · 2024
Personalized news recommendation is a crucial technology for helping users discover news articles tailored to their interests. Key challenges in this field include modeling user preferences based on implicit behaviors, accounting for the influence of the news agenda on user interests, and managing the rapid decay of news items. The ACM RecSys Challenge 2024, organized by Ekstra Bladet, provide a large-scale news dataset for benchmarking news recommendation research. In this paper, we present our solution to the challenge. We propose real-time feedback learning mechanisms to capture users’ immediate interests and explore generative sequence modeling techniques to learn impression-level user behaviors. Furthermore, we develop an ensemble method to combine tree models and deep models to improve recommendation accuracy. Based on this solution, our team ("hrec") achieved an impressive AUC score of 0.8667 on the final test set, securing the fifth place in the competition.