Dynamic Category Boost Ranking for E-commerce Search Relevance
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Solution Overview
Problem
E-commerce platforms face challenges in presenting item listings effectively to maximize transaction likelihood, as current algorithms often favor one seller over others, lead to gaming, and fail to optimize conversion rates, resulting in a suboptimal user experience and reduced sales.
Innovation Solution
A system that assigns category boost scores to item listings based on historical click data, promoting listings in relevant leaf-level categories, thereby enhancing their ranking and positioning in search results, while maintaining fairness among sellers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a simple and rigid algorithm (e.g., first-listed first-presented) is used for presenting item listings, then the system is easy to implement and manage, but some sellers may attempt to game the system, negatively impacting other sellers, buyers' experience, and the enterprise itself
Solution Approach 1:
The patent implements a dynamic ranking algorithm that adjusts item listing presentation based on multiple factors including seller performance metrics, item quality, and historical data. This dynamic approach replaces rigid first-listed algorithms with adaptive ranking that responds to seller behavior and item characteristics, preventing gaming while maintaining implementation feasibility through automated scoring systems
Solution Approach 2:
The system changes multiple parameters simultaneously to achieve fair ranking: it adjusts weighting factors for different seller metrics, modifies ranking scores based on item attributes, and dynamically tweaks presentation order based on real-time performance data. This multi-parameter adjustment allows the system to optimize fairness and conversion rates without requiring complete algorithmic redesign
2Stability of the object's composition
If item listings are presented in accordance with an algorithm that cannot easily be altered or tweaked, then the system maintains consistency, but the enterprise cannot optimize the presentation of item listings to improve the overall conversion rate
Solution Approach 1:
The ranking algorithm is designed to be dynamically adjustable, allowing the enterprise to modify weighting factors, threshold values, and scoring parameters without changing the core algorithmic structure. This enables continuous optimization of conversion rates while maintaining system consistency through standardized adjustment procedures and automated re-ranking processes
3Productivity
If a preference is given to one seller such that the one seller's item listings are consistently being presented in the most prominent position(s), then that seller benefits, but other sellers may not participate, ultimately having a negative impact on the enterprise
Solution Approach 1:
The ranking system applies different weighting factors and scoring criteria to different sellers based on their individual performance characteristics, item quality metrics, and category-specific factors. This localized approach allows high-performing sellers to receive preferential treatment in their specific areas of strength while maintaining fair presentation opportunities for other sellers in different categories or performance areas, ensuring diverse seller participation
Solution Approach 2:
The system dynamically adjusts seller preferences based on real-time performance data, allowing sellers to gain prominence through demonstrated quality and customer satisfaction rather than static favoritism. This dynamic preference system encourages ongoing seller participation and improvement while preventing any single seller from monopolizing prominent positions indefinitely
4Quantity of substance
If the number of item listings that satisfy the potential buyer's query far exceeds the number of item listings that can practically be presented, then comprehensive search results are available, but the presentation becomes overwhelming and difficult to manage
Solution Approach 1:
The system segments the large set of search results into multiple organized pages or groups, applying the ranking algorithm to prioritize listings within each segment. This segmentation allows comprehensive coverage of all matching items while managing presentation complexity through structured pagination and category-based organization, making the results digestible for buyers
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. A query to identify a set of item listings is processed where each item listing associated with an item or service being offered for sale and assigned to a leaf-level category. The scope of the query is determined based on a dictionary of queries or a length of the query when the query is not found in the dictionary. One or more categories are identified based on the scope of the query. A search results page is presented with the item listings from the identified one or more categories.