Precomputed Item Feature Expressions for Efficient Recommendation

The patent segments the recommendation system into two distinct modules: a recall module that handles large-scale item retrieval and a click rate estimation module that performs precise ranking. This segmentation allows each module to specialize in its strength - the recall module processes large quantities of items efficiently while the click rate estimation module ensures high accuracy for final recommendations, thereby resolving the contradiction between handling large numbers of users/items and maintaining recommendation accuracy.

The patent introduces an intermediary feature expression database that stores pre-computed item feature expressions. This intermediary structure acts as a buffer between the large-scale data processing and the accuracy-critical ranking operations. By pre-computing and storing feature expressions, the system reduces real-time calculation complexity and errors while maintaining high throughput, effectively resolving the contradiction between productivity and reliability.

The patent applies preliminary action by pre-computing and storing item feature expressions in the feature expression database before the actual recommendation process. This allows the system to avoid redundant calculations during real-time recommendations, reducing the calculation amount required while maintaining high recommendation accuracy through the use of pre-processed feature information.