Region-Agnostic Item Catalog Using ML Availability Matching
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Solution Overview
Problem
Online systems face challenges in presenting users with items available in their geographical region due to lack of geographical location data from users, leading to difficulties in fulfilling orders for unavailable items.
Innovation Solution
An online system generates a region- and source-agnostic catalog using machine learning, predicting item similarities and availability across multiple geographical regions, and selecting nodes with threshold availability for inclusion in the catalog.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the online system uses geographical location data to populate ordering interfaces with region-specific items, then item availability accuracy is improved, but data privacy requirements and system complexity increase
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that processes item data and availability information to generate region-agnostic item groups. This mediator enables the system to provide region-specific item recommendations without directly handling or storing sensitive geographical location data, thus maintaining reliability while reducing system complexity and privacy concerns
Solution Approach 2:
The system creates simplified copies of geographical information by using item availability patterns and characteristics to represent regional differences. Instead of working with complex location data, the system uses item groupings and availability scores that capture regional availability patterns without requiring direct access to user location information
2Ease of operation
If the online system presents items without verifying regional availability, then ordering interface simplicity is improved, but order fulfillment difficulty increases
Solution Approach 1:
The system performs preliminary availability assessment by using the machine learning model to evaluate item availability across different regions before presenting items to users. This advance checking ensures that items presented in the ordering interface are likely to be available, maintaining simplicity while improving fulfillment reliability
Solution Approach 2:
The system implements feedback mechanisms where availability information from multiple regions is continuously fed into the machine learning model. This feedback loop allows the system to learn from actual availability patterns and improve its predictions, ensuring that the ordering interface presents items with high probability of availability without complicating the user experience
3Reliability
If the online system collects geographical location data from users, then item availability matching is improved, but user privacy concerns increase
Solution Approach 1:
The machine learning model serves as an intermediary that processes availability information without requiring direct access to user geographical location data. The model learns regional availability patterns from aggregated data and applies this knowledge to match items with users based on their delivery locations, improving matching reliability while preserving user privacy
Solution Approach 2:
The system extracts and utilizes only the necessary availability characteristics from regional data without collecting or storing sensitive user location information. By taking out and processing only the essential item availability patterns, the system achieves good availability matching while minimizing privacy intrusion
Data Source
AI summary
An online system retrieves item data for items available at sources in multiple regions and generates candidate nodes based on the item data, in which each candidate node represents items having at least a threshold measure of similarity to each other. The system accesses and applies a machine-learning model to predict a matching score for each combination of an item and a candidate node based on item data for the item and attributes of items represented by the candidate node. The system assigns the items to candidate nodes based on the matching scores, retrieves information describing an availability of each item in each geographical region, and identifies an average availability of items assigned to each candidate node across the geographical regions. The system selects nodes to include in a region- and source-agnostic item database, in which the average availability associated with each selected node is at least a threshold.