Category Demand Normalized Search Ranking
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Solution Overview
Problem
E-commerce platforms face challenges in presenting search results in a way that maximizes transaction likelihood, as existing algorithms can be too rigid, leading to unfair promotion of certain sellers, potential gaming of the system, and a lack of optimization for conversion rates, ultimately affecting buyer experience and enterprise success.
Innovation Solution
Implementing a system that assigns a normalized category boost score to item listings based on historical click data, using a tree-like hierarchy of categories and click probability scores to promote listings in relevant categories, thereby optimizing their position in search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a simple and rigid algorithm (e.g., first-listed first-presented) is used for presenting item listings, then the system is easy to operate and implement, but it prevents optimization of conversion rates and allows sellers to game the system
Solution Approach 1:
The patent implements a dynamic ranking algorithm that adjusts item listing positions based on multiple factors including seller performance metrics, item characteristics, and category demand. This dynamic system replaces rigid first-listed-first-presented algorithms with adaptive ranking that optimizes conversion rates while preventing seller manipulation through continuous adjustment based on performance data.
Solution Approach 2:
The system changes multiple parameters simultaneously including seller feedback scores, item condition ratings, pricing competitiveness, and category-specific demand factors. By adjusting these parameters dynamically, the algorithm optimizes search result presentation to maximize conversion rates while maintaining fairness and preventing gaming of the system.
2Productivity
If item listings are presented in prominent positions based on a preference for one seller, then that seller benefits from increased visibility, but other sellers may not participate and the enterprise ultimately suffers
Solution Approach 1:
The patent applies different ranking weights and performance metric requirements to different sellers and item categories based on their specific characteristics. This localized approach allows prominent positioning of high-performing items while ensuring fair participation opportunities for all sellers through category-specific adjustments and performance-based ranking that adapts to individual seller capabilities.
3Stability of the object's composition
If a rigid algorithm cannot be easily altered or tweaked, then the system maintains consistency, but it negatively impacts the enterprise when sellers attempt to game the system
Solution Approach 1:
The system pre-establishes multiple performance metrics, weighting factors, and adjustment parameters before operation begins. These preliminary configurations allow the algorithm to maintain consistent baseline operations while enabling administrators to adjust weights and parameters in response to seller gaming attempts or performance optimization opportunities, balancing stability with adaptability.
Data Source
AI summary
Described herein are methods and systems for promoting item listings that satisfy a query based on the item listings being assigned to certain categories that have, based on historical click data, exhibited high demand characteristics for the query. Consistent with some embodiments, a certain number of leaf-level categories are identified based on demand data for those categories, and the item listings assigned to those categories are promoted through a normalized weighting factor derived in part based on the click probability score associated with the category, clicks per impression rate, and weighted clicks per impression by ranking rate.