Lookalike Modeling via Segment Fingerprinting
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Solution Overview
Problem
Existing lookalike modeling techniques in cloud platforms are computationally expensive, rely on pre-built segments, and overfit users, especially when dealing with large segments or high-dimensional attributes, limiting their effectiveness for new or small organizations.
Innovation Solution
A lookalike modeling service that uses a combination of cluster modeling and combinatorial modeling to generate candidate segments and calculate similarity scores based on divergence scores, allowing for efficient profiling and identification of lookalike users without requiring pre-built segments, and handling hyper-dimensional features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lookalike modeling techniques are used, then user similarity can be identified, but computational cost becomes excessively high
Solution Approach 1:
The patent segments the user base into distinct clusters using unsupervised learning algorithms. Instead of computing pairwise similarities across all users, the system divides users into manageable segments (clusters) based on shared characteristics, thereby reducing the computational complexity from O(n²) to O(n) while maintaining similarity identification accuracy.
Solution Approach 2:
The patent transforms the similarity measurement approach by changing parameters from direct user-to-user comparison to cluster-to-cluster comparison. By representing each cluster with aggregated features and computing similarity between cluster centroids rather than individual users, the system significantly reduces computational cost while preserving the essential similarity information.
2Ease of manufacture
If pre-built segments are used for lookalike modeling, then modeling can be performed, but the system cannot handle new or small organizations without existing segments
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically creates segments from raw user data using unsupervised learning. Instead of requiring pre-built segments as input, the system autonomously discovers user groupings and patterns, enabling new or small organizations to immediately benefit from lookalike modeling without needing pre-existing segment infrastructure.
Solution Approach 2:
The patent performs preliminary segment creation automatically before the user needs lookalike modeling. By pre-processing user data to create clusters and segment representations in advance, the system ensures that when lookalike modeling is requested, the necessary segment structure already exists, eliminating the need for manual segment building and enabling immediate use for new organizations.
3Measurement precision
If traditional modeling approaches are used, then lookalike users can be identified, but the models overfit to seed segments
Solution Approach 1:
The patent extracts the essential characteristics and patterns from the seed segment and applies them to identify lookalike users across the broader population. By separating the seed segment analysis from the lookalike identification process and using the extracted patterns as guidance rather than direct templates, the system maintains generalization capability while achieving precise lookalike user identification.
Solution Approach 2:
The patent introduces dynamic adaptability by allowing the model to adjust its behavior based on the relative sizes of seed and target segments. When target segments are small, the model adapts to avoid overfitting by incorporating regularization and by using the seed segment more as a directional guide rather than a strict template, thereby maintaining reliability across different segment size scenarios.
4Measurement precision
If high-dimensional user attributes are processed, then more accurate similarity can be achieved, but computational complexity increases
Solution Approach 1:
The patent segments the high-dimensional feature space by grouping related attributes and clustering users based on these grouped features. Instead of processing all high-dimensional attributes simultaneously for every user comparison, the system divides the feature processing into manageable segments corresponding to user clusters, reducing computational complexity while preserving the ability to capture nuanced similarities across multiple dimensions.
Data Source
AI summary
Methods, systems, apparatuses, devices, and computer program products are described. A modeling service may generate a set of candidate segments using a set of cluster models and based on a seed segment and entity data. Based on respective features associated with the segments, the service may generate candidate segment fingerprints and a seed segment fingerprint, where a segment fingerprint may indicate a distribution of entities within a segment based on similarities between features associated with entities within the segment. That is, a segment fingerprint may depict how similar entities are in a candidate segment based on different features. The service may calculate similarity scores between the seed segment and the candidate segments using the segment fingerprints, and rank entities in terms of their similarity. The highest ranking entities may be identified from the candidate segments and included in a lookalike segment corresponding to the seed segment.