Cross-scenario Multi-modal Knowledge Fusion and Knowledge Recommendation Based on a MDR-DKD Model

With the widespread application of recommendation systems in e-commerce, education, and other fields, the heterogeneity of cross-scenario data and the insufficient integration of multi-modal information such as text, images, and user behavior are becoming increasingly prominent. To achieve cross-scenario multi-modal knowledge fusion and knowledge recommendation, a meta doubly robust-debiasing knowledge distillation (MDR-DKD) model is proposed. This model efficiently extracts universal features cross-scenarios using a small amount of unbiased data through a meta-learning mechanism and optimizes the model by combining knowledge distillation techniques. Finally, combined with the knowledge recommendation module, targeted knowledge recommendation is achieved by calculating the matching degree between user interests and knowledge nodes. The results showed that the multi-modal feature extraction of the model took an average of 18.61 ms, the parameter utilization rate during the feature extraction process was 91.3%, the feature extraction throughput reached 2460 samples/s, and the knowledge recommendation accuracy was 97.84%. This model can effectively extract cross-scenario multi-modal features for accurate knowledge recommendation. The research provides an effective technical path for cross-domain knowledge recommendation, which can promote the implementation of recommendation systems in multi-scenario and multi-modal practical scenarios, and help improve the personalized recommendation experience for users.