Affinity Group Generation via Bayesian Ranking and Multinomial Classification
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Solution Overview
Problem
Current predictive modeling techniques for identifying users likely to interact with objects face scalability issues as data sets grow in size, affecting their performance in accurately predicting user interests and object interactions.
Innovation Solution
The system employs a Bayesian ranking approach using an object recommendation model, which generates object affinity groups by calculating probabilities of user interactions through a classifier and a probabilistic model, such as a beta-geometric model, to rank users based on their likelihood to interact with specific objects or attributes within a forecasting window.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If collaborative filtering techniques are used to predict user interests and identify matching objects, then prediction accuracy is improved, but scalability deteriorates as the data set increases in size
Solution Approach 1:
The patent segments users into affinity groups based on their interaction patterns with objects. Instead of analyzing all user-object relationships globally (which causes scalability issues), the system divides users into distinct groups where each group shares similar preferences. This segmentation allows the system to maintain prediction accuracy within each group while reducing the overall computational complexity as data set size increases.
Solution Approach 2:
The patent changes the approach from direct collaborative filtering to a parameter-based affinity group classification system. By transforming the problem into identifying user affinity groups through classification parameters rather than computing all pairwise user similarities, the system achieves both scalability and prediction accuracy. The affinity group labels serve as parameters that enable efficient recommendation generation.
2Adaptability or versatility
If the data set increases in size to improve coverage and diversity of user interests, then prediction comprehensiveness is improved, but system performance deteriorates due to scalability problems
Solution Approach 1:
As data set size increases to improve coverage, the patent uses segmentation to automatically organize users into affinity groups. This prevents the system from becoming overwhelmed by the increased data volume, as the segmentation process structures the growing data into manageable groups that maintain computational efficiency while capturing diverse user interests.
Solution Approach 2:
The affinity group classification system serves multiple functions simultaneously: it segments users for scalability, captures diverse interests for comprehensiveness, and enables efficient recommendation generation. This multi-functional approach allows the system to handle large data sets while maintaining both performance and prediction comprehensiveness.
Data Source
AI summary
The example embodiments are directed toward improvements in generating affinity groups. In an embodiment, a method is disclosed comprising generating probabilities of object interactions for a plurality of users, a given object recommendation ranking for a respective user comprising a ranked list of object attributes; calculating interaction probabilities for each user over a forecasting window; calculating affinity group rankings based on the probabilities of object interactions and the interaction probabilities for each user; and grouping the plurality of users based on the affinity group rankings.