Listing Recommendation Engine for Network Commerce
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network-based commerce systems lack an efficient method to provide user-friendly listing recommendations to users based on their past interactions and popular search terms, leading to suboptimal user experience and inefficient browsing.
Innovation Solution
A method and system that generate listing recommendations by identifying user interactions, creating recommendation queries using popular search terms and categories, and presenting relevant listings to users, incorporating a popularity threshold and relative popularity boundaries to rank and filter recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current network-based commerce systems present information to users using conventional methods, then the system structure remains simple, but the user experience is suboptimal and browsing efficiency is low
Solution Approach 1:
The system performs preliminary actions by pre-computing popularity metrics, search term frequencies, and category relationships before users arrive. Recommendation queries are pre-generated based on historical data, and popularity thresholds are pre-calculated, allowing the system to quickly retrieve and present personalized recommendations without complex real-time computations
Solution Approach 2:
The patent introduces an intermediary recommendation layer between the user and the full listing database. This intermediary system uses popularity metrics and search term analysis to filter and rank listings, presenting a curated subset that improves user experience while managing system complexity through abstraction
2Productivity
If the system provides comprehensive listing information to all users, then information completeness is maintained, but users spend excessive time browsing and finding desired products
Solution Approach 1:
The system applies local quality by providing different levels of information presentation to different users based on their behavior patterns. Instead of uniform comprehensive listings, the system tailors the presentation by highlighting relevant categories, popular search terms, and personalized recommendations, allowing users to quickly locate desired products without examining all available information
Solution Approach 2:
The patent changes parameters by dynamically adjusting recommendation criteria based on popularity metrics, search term frequencies, and user interaction patterns. The system modifies listing rankings and visibility parameters in real-time based on computed popularity thresholds, enabling efficient product discovery while maintaining information completeness in the underlying database
3Adaptability or versatility
If the system implements personalized recommendations based on user interactions, then user engagement improves, but the complexity of tracking and analyzing user behavior increases
Solution Approach 1:
The system performs preliminary analysis of user interactions by pre-computing popularity metrics and search term frequencies from historical data. User behavior patterns are analyzed in advance to establish baseline recommendations, reducing the complexity of real-time personalization while maintaining adaptability to user preferences
Solution Approach 2:
The patent implements feedback mechanisms where user interactions with recommended listings are tracked and fed back into the popularity computation system. This feedback loop continuously refines the recommendation algorithm by updating search term frequencies and category popularities, enabling the system to adapt to changing user preferences without requiring complex real-time processing
Data Source
AI summary
According to one aspect of the invention, there is provided a method to facilitate generating listing recommendations to a user of a network-based commerce system. In one embodiment, the method includes identifying a term associated with a user interaction in a network-based commerce system. The method further includes generating a recommendation query including the identified term. In addition, the method includes running the recommendation query against a plurality of listings hosted by the network-based commerce system to identify a recommendation. Moreover, the method includes presenting the recommendation to a user of the network-based commerce system.