Cluster-Based User Matching System for Reducing Computational Cost

The patent segments the user base into clusters based on feature data similarity. Instead of calculating similarities between all individual users, the system divides users into groups (clusters) and performs calculations at the cluster level. This segmentation reduces the computational complexity from O(n²) for all-user comparisons to O(k²) for cluster comparisons, where k is the number of clusters, significantly reducing computational time while preserving matching accuracy.

The patent introduces clusters as intermediary entities between individual users and the recommendation system. Rather than directly comparing user feature data, the system uses cluster representations as intermediaries. Each cluster serves as a mediator that aggregates user similarities, allowing the system to infer user preferences through cluster memberships without performing exhaustive pairwise user comparisons.

The system segments the computational workload by organizing users into clusters. This segmentation allows the system to process feature data in grouped batches rather than individual user records, reducing memory access patterns and improving cache utilization. The clustering structure enables efficient aggregation of user features at the group level, reducing overall computational resource consumption while maintaining the ability to provide accurate matching information.

The patent merges individual user feature data into cluster-level representations. By combining similar users into single clusters and computing aggregate features for each cluster, the system reduces the total volume of data that needs to be processed. This merging operation consolidates redundant information across multiple users, decreasing memory usage and computational resources required for similarity calculations while preserving the essential patterns needed for accurate recommendations.