Dwell Time Ranking Algorithm for E-Commerce Search Results
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
E-commerce platforms face challenges in presenting search results in a way that maximizes transaction likelihood, as rigid algorithms can lead to unfair favoritism, gaming, and low conversion rates, while failing to account for user engagement and seller performance.
Innovation Solution
A system that ranks search results based on dwell time, combining historical user interaction data, relevance, and business rules to dynamically position listings, promoting relevant and high-quality items while encouraging seller compliance.
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 maintain, but it leads to unfair favoritism, gaming, and low conversion rates
Solution Approach 1:
The patent implements a dynamic ranking algorithm that adjusts listing positions based on multiple factors including seller performance metrics, buyer behavior data, and relevance scores. This dynamic system replaces static first-listed-first-presented algorithms with adaptive ranking that responds to real-time data, thereby improving conversion rates while maintaining fairness through objective criteria
Solution Approach 2:
The system changes multiple parameters simultaneously including relevance scores, seller performance metrics, buyer preferences, and engagement data to determine listing rank. By adjusting these parameters dynamically rather than relying on a single fixed criterion, the system achieves both fairness and high conversion rates
2Reliability
If item listings are presented in accordance with a rigid algorithm, then the system is transparent and predictable, but some sellers may attempt to game the system, negatively impacting other sellers, buyers' experience, and the enterprise
Solution Approach 1:
The patent implements feedback loops where buyer behavior data, engagement metrics, and transaction outcomes continuously inform and adjust the ranking algorithm. This feedback mechanism makes it difficult for sellers to game the system because ranking factors are based on actual buyer responses rather than manipulatable static criteria, while maintaining predictability through consistent application of objective metrics
Solution Approach 2:
The ranking system incorporates multiple universal criteria including relevance, seller performance, buyer preferences, and engagement data that apply across all sellers and listings. This multi-functional approach ensures fair treatment of all participants while reducing opportunities for gaming through diversified evaluation dimensions
3Productivity
If a preference is given to one seller so that their item listings are consistently presented in the most prominent positions, then that seller benefits from increased visibility, but other sellers may not participate, ultimately having a negative impact on the enterprise
Solution Approach 1:
The patent applies different ranking weights and criteria to different sellers based on their performance metrics, relevance, and buyer feedback. Rather than uniform treatment or preferential treatment of single sellers, the system tailors ranking parameters to each seller's characteristics and performance, allowing multiple sellers to achieve prominent positions through merit while maintaining overall system diversity and participation
Data Source
AI summary
A method and a system to rank search results based on dwell time is provided. The system comprises a search module to identify a plurality of listings stored in a listing database as search results. A dwell time module determines a respective dwell time associated with each of the plurality of listings. The dwell time is based on an elapsed amount of time one or more buyers view a view item page associated with the listing. A ranking module ranks the listings composing the identified plurality of listings based at least in part on the respective dwell time associated with each of the plurality of listings.