The application provides an information recommendation method,
system, device and storage medium, comprising: by associating a special graph structure for different expert networks, the physical and logical decoupling of
modal information is realized, and the embedding feature information of the target user and the candidate item in different semantic dimensions can be more accurately described. By calculating the data routing distribution with the user-item interaction instance as the
granularity, and combining the progressive routing strategy with the prior routing distribution to obtain a
hybrid routing distribution, accurate expert scheduling can be realized in different context environments, that is, it can automatically identify which
modal features play a key role in user decision-making in a specific
scenario, thereby accurately capturing the fine-grained preferences of users. Through the gating selection mechanism, the feature contribution of key experts is retained, effectively filtering the
noise interference generated by irrelevant modalities, and further improving the accuracy of the
prediction score and the reliability of the recommendation result.