Recommendation Service Using Cluster-Level Random Effects
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Solution Overview
Problem
Existing job matching and recommendation services face inefficiencies due to the complexity of maintaining personalized models for individual members and jobs, especially when dealing with large datasets and the 'cold start' issue of new members or jobs with limited data, and struggle with segmenting member and job populations into manageable clusters.
Innovation Solution
The implementation of a generalized linear mixed model (GLMix) that uses cluster-level random effects instead of individual-level effects, allowing for the formation of equi-sized clusters through matrix factorization and balanced clustering, which reduces model complexity and addresses the cold start problem by grouping similar members and jobs into clusters for more efficient recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personalized models are maintained for individual members and jobs, then recommendation accuracy is improved, but model complexity and computational intensity increase
Solution Approach 1:
The patent segments the member and job populations into distinct clusters based on shared characteristics and behaviors. Instead of maintaining personalized models for every individual member or job, the system creates cluster-level models that represent groups of similar entities. This segmentation reduces the number of models needed while preserving personalized recommendations through cluster-specific patterns.
Solution Approach 2:
The patent merges individual member and job characteristics into cluster-level representations by aggregating data from multiple individuals within each cluster. This combining approach allows the system to capture common patterns and behaviors at the cluster level, reducing computational complexity while maintaining recommendation quality through group-level personalization.
2Ease of operation
If individual-level random effects are used in the model, then personalized recommendations are achieved, but computational intensity and processing time increase
Solution Approach 1:
The patent segments the population into clusters and applies random effects at the cluster level rather than the individual level. This segmentation reduces the number of random effect parameters from potentially thousands of individuals to a manageable number of clusters, significantly decreasing computational intensity and processing time while preserving personalized recommendations through cluster-specific effects.
3Adaptability or versatility
If the member population is segmented into concrete segments, then tailored recommendations are provided, but architecture and code complexity increase over time
Solution Approach 1:
The patent implements a universal clustering framework that can handle multiple segmentations and population divisions through a single cohesive architecture. Rather than creating separate code paths for different segment types, the system uses a unified clustering approach that adapts to various segmentation needs, reducing architectural complexity while maintaining versatility in providing tailored recommendations.
4Productivity
If cluster-level random effects are implemented, then computational efficiency is improved, but individual-level personalization may be reduced
Solution Approach 1:
The patent applies local quality by making recommendations personalized at the cluster level rather than uniformly across all individuals. Each cluster receives tailored recommendations based on its specific characteristics and patterns, providing locally optimized personalization that balances computational efficiency with recommendation precision. This approach ensures that while not every individual has a unique model, each cluster receives customized treatment appropriate to its members' shared behaviors.
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. A matrix used in the matrix factorization is customized based on the recommendation type. 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.