Multimodal Interest-shifting Sequence Recommendation Algorithm Based on Reinforcement Learning

To address the critical challenges in the accurate "expert-project" matching within intelligent review scenarios—specifically the dynamic drift of experts’ research interests over time and the inadequacy of single-modal information in characterizing complex scientific research content—and to solve the issue where existing sequential recommendation methods fail to adapt to sudden interest changes in real-time due to offline training, this paper proposes an Intelligent Review Sequential Recommendation Algorithm based on Reinforcement Learning and Multi-modal Interest Drift (MM-DRLSR). Closely aligning with the construction requirements of the intelligent review system for the National Independent Software and Hardware Ecosystem Verification Public Service Platform, the algorithm first models the latent distribution of experts’ research direction drift through drift degree quantification and a disentanglement mechanism, employing a drift-aware alignment module to capture collaborative research relationships among experts. Secondly, a lightweight attention fusion module is designed to integrate multi-modal features, including texts and charts from project proposals, thereby enhancing project representation. Finally, combined with dynamic reinforcement learning, the matching strategy is optimized online utilizing review feedback. Experiments on public datasets demonstrate that MM-DRLSR achieves a 16.7% to 23.0% improvement in Recall and NDCG metrics over strong multi-modal baselines and adapts rapidly in sudden drift simulations. These results validate the algorithm’s effectiveness in resolving expert interest drift and precise matching issues, providing efficient algorithmic support for expert recommendation in intelligent review systems.