Algorithmic Featured Product Group Selection
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Solution Overview
Problem
Manual selection and updating of featured product subcategories by merchants or product experts is costly and inefficient, especially for large product repositories with frequent changes in inventory and consumer behavior.
Innovation Solution
An algorithmic method that automatically generates featured product groups by analyzing product attributes and user engagement, determining relevance scores, and selecting a subset of attributes to present a featured set of product groups on a website, reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of featured subcategories is performed by merchants or product experts, then relevant product groups can be identified, but the cost and time consumption increase significantly
Solution Approach 1:
The system enables automatic selection of featured product groups through algorithms that analyze product attributes, sales data, and user behavior patterns without requiring manual intervention from merchants or product experts. The system serves itself by autonomously identifying and updating relevant product groups based on real-time data.
Solution Approach 2:
The patent replaces the manual mechanical process of selecting and updating product subcategories with an automated computational system. Algorithms process product data, calculate relevance scores, and generate featured product groups automatically, substituting human labor with machine-based intelligence.
2Measurement precision
If manual selection of featured subcategories is performed, then product relevance is maintained, but the scalability to large product repositories is limited
Solution Approach 1:
The automated system is designed to handle diverse product repositories across multiple categories and scales. The same algorithmic framework can process everything from small to large product inventories, making the solution universally applicable regardless of the size or type of e-commerce platform.
Solution Approach 2:
The system adapts to different product repository sizes by dynamically adjusting processing parameters such as the number of featured groups to display, relevance score thresholds, and data sampling rates. This allows the same system to efficiently scale from small to large product catalogs without requiring manual reconfiguration.
3Extent of automation
If automated selection of attribute values is implemented, then manual effort is reduced, but the requirement for manual attribute selection remains
Solution Approach 1:
The system automatically discovers and selects relevant product attributes by analyzing the product data structure, user search patterns, and conversion metrics. Instead of requiring merchants to pre-define which attributes to use, the system self-determines the most valuable attributes for generating featured product groups based on observed user behavior and sales data.
4Adaptability or versatility
If frequent updates of featured subcategories are performed manually, then relevance to current consumer behavior is maintained, but operational costs increase
Solution Approach 1:
The system continuously monitors product sales data, user behavior patterns, and inventory changes in real-time, automatically updating featured product groups without interruption. This continuous operation maintains relevance to current consumer behavior while eliminating the need for periodic manual updates, thereby reducing operational costs.
Solution Approach 2:
The system incorporates feedback loops where user interactions with featured product groups, click-through rates, and purchase conversions are continuously analyzed. This feedback informs automatic adjustments to the featured groups, ensuring they remain aligned with current consumer preferences while reducing manual intervention costs.
Data Source
AI summary
A method of computing a featured set of product groups for a query on an inventory of products. Each of the products can have one or more attribute-value pairs. Each of the one or more attribute-value pairs can have an attribute. The method can include determining a result group of the products matching the query. The method also can include determining relevance scores for the product groups. The method further can include determining a featured attribute and the featured set of the product groups for the featured attribute. The method also can include, after receiving the query from a user, transmitting for display at least one page of a website, where the at least one page has an option to view each of the product groups of the featured set of the product groups that has been selected. Other embodiments of related systems and methods are also disclosed.