Equi-Sized Cluster Partitioning for Recommendation Model Complexity
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Solution Overview
Problem
Existing job matching and recommendation services face inefficiencies due to complex architectures and difficulty in maintaining individual-level coefficients, especially when dealing with large datasets and intersecting member segments, leading to challenges in personalization and cold start issues.
Innovation Solution
The implementation of a generalized linear mixed model (GLMix) with cluster-level random effects, using matrix factorization and balanced clustering to partition members and jobs into equi-sized clusters, allowing for more efficient computations and personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual-level coefficients are used for personalization in job matching services, then recommendation accuracy is improved, but model complexity and computational burden increase significantly
Solution Approach 1:
The patent segments the member population into distinct clusters (e.g., active job-seekers, talent seekers, executives) and creates separate service models for each segment. This segmentation allows the system to maintain personalized recommendations for each group while avoiding the complexity of individual-level coefficients, as each cluster is served by a dedicated model rather than requiring unique parameters for every member.
Solution Approach 2:
The patent introduces cluster segments as an intermediary layer between individual members and the recommendation system. Instead of directly managing individual-level coefficients for all members, the system uses cluster-level models as intermediaries that capture shared characteristics of groups, thereby reducing overall model complexity while preserving personalization capabilities at the cluster level.
2Ease of operation
If concrete segments are defined for different member groups, then personalized service delivery is improved, but system maintainability and understanding become difficult
Solution Approach 1:
The patent creates a universal clustering framework that can accommodate multiple member segments (executives, active job-seekers, talent seekers, etc.) using a common architecture. This universal approach allows the system to maintain personalized service for diverse segments while using a standardized, easily maintainable codebase that can serve all segments through the same clustering and model generation mechanisms.
3Adaptability or versatility
If segment definitions are expanded to cover more member categories, then service coverage is improved, but computational efficiency decreases
Solution Approach 1:
The patent segments the large and diverse member population into distinct, manageable clusters. By organizing members into these segments, the system can efficiently process and serve each group with tailored models without the computational overhead of handling every individual case, thus maintaining high computational efficiency while expanding service coverage to diverse member categories.
4Ease of manufacture
If legacy services are used to serve defined segments, then implementation simplicity is maintained, but ability to leverage intersecting segments is lost
Solution Approach 1:
The patent implements a dynamic clustering approach where member segments are not fixed but can be redefined and reclustered based on current data and requirements. This dynamic system allows members to belong to different clusters depending on their behavior and characteristics, enabling the system to naturally handle intersecting segments (where a member might fit multiple traditional categories) while maintaining implementation simplicity through automated clustering algorithms.
Data Source
AI summary
The disclosed subject matter involves identifying clusters and segments of a population of data for use in a recommendation service. Clusters of members or items are formed, where the clusters, or partitions are close to being equal in size. Items are distributed based on similarities identified with matrix factorization. The items are formed into clusters based on the similarities and the clusters are used in training of a generalized linear mixed model treating the clusters as random-level effects. The trained model may be used in the recommendation service. Other embodiments are described and claimed.