Online Resource Recommendation via Time-Weighted Frequency Analysis

The patent segments the access frequency calculation by dividing the time dimension into multiple time periods (e.g., recent period, historical period). Each time period's access frequency is calculated separately and then weighted combined, rather than calculating a single overall access frequency. This segmentation allows the system to capture temporal patterns in user behavior while maintaining calculation efficiency through modular processing.

The patent performs preliminary actions by pre-calculating and storing access frequency data for different time periods when data is available. When generating resource recommendations, the system retrieves these pre-computed frequencies and applies weighting factors, rather than performing complex real-time calculations. This preliminary preparation significantly reduces the time required for recommendation generation while maintaining high accuracy.

The patent applies local quality by assigning different weighting factors to different time periods based on their relative importance. Recent time periods are assigned higher weights to reflect their greater relevance to current user preferences, while historical periods receive lower weights. This non-uniform weighting scheme preserves temporal information and ensures that the recommendation system adapts to changing user behavior patterns while maintaining operational simplicity through a clear weighting mechanism.