Vectorized Merchant Scoring for Latent Preference Capture
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Solution Overview
Problem
Current systems for real-time on-demand delivery of perishable goods struggle to provide personalized merchant recommendations as they rely on explicit data and do not effectively capture latent user preferences, leading to data sparsity issues in local marketplaces.
Innovation Solution
A delivery logistics system utilizing a predictive merchant ranking model that employs a machine learning model to generate dynamic scoring predictions by extracting feature values from customer and merchant profiles, converting them into vectors, and adjusting weighted coefficients based on customer activity, thereby providing targeted recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If explicit data from purchase history and category information are used to identify customer preferences, then the system can provide basic merchant recommendations, but it fails to capture latent user preferences leading to data sparsity issues
Solution Approach 1:
The patent transforms merchant and customer data from traditional tabular formats into vector representations in a multi-dimensional space. This dimensional transformation allows the system to capture latent preferences and characteristics that are not apparent in explicit data, thereby reducing information loss and improving recommendation accuracy through vector similarity calculations.
Solution Approach 2:
The patent introduces vector representations as an intermediary layer between explicit customer-merchant data and recommendation outcomes. These vectors serve as mediators that encode latent preferences and merchant characteristics, enabling the system to make accurate recommendations even when direct purchase history data is sparse.
2Adaptability or versatility
If traditional category-based preference identification is used, then the system can operate with simple data structures, but it cannot provide personalized recommendations for real-time on-demand delivery
Solution Approach 1:
The patent replaces traditional mechanical data processing methods (category-based filtering and matching) with machine learning-based vectorization techniques. This substitution enables the system to automatically learn and adapt to individual customer preferences from available data, providing personalized recommendations without requiring complex manual data structuring or categorical frameworks.
3Quantity of substance
If only purchase history data is considered for customer preferences, then the system can maintain simple data requirements, but it misses implicit data that could improve recommendation quality
Solution Approach 1:
The patent creates a universal vectorization framework that can process multiple types of data (purchase history, browsing behavior, demographic information, and other implicit data) through a single cohesive mechanism. This multi-functional approach allows the system to effectively utilize all available data sources, transforming diverse data types into comparable vector representations that collectively improve preference identification accuracy.
Data Source
AI summary
Provided are various mechanisms and processes for generating dynamic merchant scoring predictions. A system is configured to receive datasets comprising pairings between training customer profiles and training merchant profiles. For each pairing, a set of feature values corresponding to features specified by the customer and merchant profiles are extracted and converted into a training vector to train a machine learning model to determine a weighted coefficient for each feature. Once sufficiently trained, the system determines a set of available merchant profiles for a customer profile in response to receiving a search request from a customer associated with the customer profile. For each pairing between the customer profile and an available merchant profile, the system determines an order score for the available merchant based on the weighted coefficients and an input set of feature values specified by the customer profile and the available merchant profile.