A hybrid expert model sparse inference method and system for generative recommendation
By employing a hybrid expert model sparse inference method specifically designed for recommendation scenarios, and combining hierarchical attention and sparse attention mechanisms with a load-balanced loss function, we optimize expert selection and computational resource allocation. This solves the computational efficiency and accuracy issues of generative recommendation models in real-time inference, and achieves a highly efficient recommendation system.
Patent Information
- Application Number
- CN202610305104.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-13
- Publication Date
- 2026-06-23
- Estimated Expiration
- 2046-03-13
AI Technical Summary
Existing generative recommendation models struggle to balance computational efficiency and inference performance, especially in real-time inference with hybrid expert models. They cannot fully utilize the parallel computing capabilities of hardware, resulting in high inference latency and limited model accuracy.
We design a hybrid expert model sparse inference method specifically for recommendation scenarios. It adopts a hierarchical attention mechanism and a sparse attention mechanism, and optimizes expert selection and computational resource allocation by decoupling the gating network and expert network through a load balancing loss function. We also achieve hardware parallel efficiency through a low-level fusion operator.
With low time overhead and low computational complexity, it achieves recommendation accuracy comparable to or even higher than dense models, improves the real-time inference performance and computational efficiency of the model, and solves the problem of balancing accuracy and efficiency in recommendation systems.