Machine Learning Listing Segmentation for Scalable Resource Allocation
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Solution Overview
Problem
Existing online marketplaces face inefficiencies in managing diverse listings due to the lack of a systematic approach for calendar data segmentation, leading to resource wastage, diminished marketing effectiveness, and poor user experience, as well as challenges in scaling operations without compromising service quality.
Innovation Solution
A scalable machine learning-driven segmentation system that uses K-means clustering and decision tree models to classify listings based on 'streaks of availability', validated by user research, and integrates the segmentation within a data warehouse using database queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If no systematic segmentation approach is used for calendar data, then all listings are treated uniformly, but this leads to resource wastage and diminished marketing effectiveness
Solution Approach 1:
The patent applies segmentation by dividing listings into distinct clusters based on calendar availability patterns using K-means clustering. This creates homogeneous groups of listings with similar availability characteristics, enabling targeted resource allocation and marketing strategies for each segment rather than treating all listings uniformly, thereby reducing resource wastage while maintaining manageable complexity.
2Productivity
If no clustering is applied to listings, then resource allocation is inefficient, but implementing clustering requires complex data processing and analysis
Solution Approach 1:
The patent replaces manual or simple rule-based listing management with an automated machine learning system. The K-means clustering algorithm automatically processes calendar data and assigns listings to segments without requiring complex manual intervention, thereby improving resource allocation efficiency while managing data processing complexity through automation.
Solution Approach 2:
The patent transforms raw calendar availability data into meaningful segmentation parameters through feature engineering and clustering. By changing the representation of listing data from raw calendar dates to clustered segments with defined characteristics, the system enables efficient resource allocation while abstracting away the complexity of raw data processing.
3Ease of operation
If calendar data is not segmented, then marketing communications can be sent to all users, but this results in unnecessary bandwidth consumption and reduced user experience
Solution Approach 1:
The patent segments the user base into distinct clusters based on their listing availability patterns. This enables targeted marketing communications to be sent only to relevant user segments rather than all users, reducing unnecessary bandwidth consumption and improving user experience by delivering relevant rather than spammy communications, while maintaining operational simplicity through automated segment identification.
4Adaptability or versatility
If the platform grows without a scalable segmentation system, then more listings can be added, but service quality deteriorates due to inability to manage diverse listings efficiently
Solution Approach 1:
The patent implements a scalable segmentation system using K-means clustering that automatically adapts as new listings are added to the platform. The system maintains service quality by continuously organizing diverse listings into homogeneous segments, enabling efficient management and targeted resource allocation regardless of platform size, thereby supporting growth while preserving reliability.
Solution Approach 2:
The patent creates a dynamic segmentation system that can adapt to changing platform conditions and grow with the platform. The clustering system can be re-executed as new data becomes available, allowing the segments to evolve and maintain their effectiveness as the platform scales, ensuring service quality is preserved through continuous optimization.
Data Source
AI summary
Systems and methods are provided to classify activities into clusters. The systems and methods access data representing activity on a network site and generate, based on the data, a plurality of feature sets representing periods of activeness on the network side. The systems and methods form a subset of the plurality of feature sets by reducing dimensionality of the plurality of feature sets. The systems and methods generate a plurality of clusters of the subset of the plurality of feature sets, each cluster being associated with a label representing a different type of activeness on the network site, and generate a database query set based on the plurality of clusters and the plurality of feature sets to classify one or more activities on the network site into one of the plurality of clusters.