Dynamic Filter Weighting for E-Commerce Interface Complexity
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Solution Overview
Problem
In a multi-seller electronic retail setting, users face difficulties in locating the best offering for an item due to the overwhelming number of attributes and filters available, making it challenging to find the most suitable combination of attributes based on their preferences.
Innovation Solution
The system generates filters dynamically based on user analysis and transaction history, weighting filters by their effectiveness to prioritize those most relevant to individual users, allowing for personalized filtering in the user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all available filters are presented to users in a multi-seller marketplace, then users have complete filtering options to find the best offering, but the interface becomes overwhelming and difficult to navigate
Solution Approach 1:
The patent segments the complete set of filters into multiple categories (e.g., price, shipping, seller ratings, item conditions). Users can selectively expand or collapse these categories, allowing them to access comprehensive filtering options when needed while maintaining a clean, manageable interface by default.
Solution Approach 2:
The filter interface is made dynamic by allowing users to customize which filter categories are displayed based on their preferences and the specific item context. The system can dynamically adjust the number and type of filters shown, transitioning between a simplified view and a comprehensive view as needed.
2Adaptability or versatility
If personalized filters are generated based on user analysis and transaction history, then the filtering experience is tailored to individual preferences, but the system complexity increases
Solution Approach 1:
The system performs self-service by automatically analyzing user behavior patterns, transaction history, and preferences to generate personalized filters without requiring manual user configuration. The system learns from user interactions and autonomously adjusts the filter set to match individual preferences, reducing the need for complex user setup procedures.
Solution Approach 2:
The system implements feedback loops where user interactions with filters and search results are continuously analyzed. This feedback is used to refine and adjust the personalized filter generation, allowing the system to adapt to changing user preferences over time while maintaining manageable complexity through iterative improvement.
Data Source
AI summary
Disclosed are various embodiments of systems, methods, and computer programs that generate filters that can be used to filter offerings in a user interface. The effectiveness of filters can be evaluated to generate an effectiveness metric. The effectiveness metric can be used to weight the filters. The effectiveness metric can be based on a probability analysis of filters that are based on the probability that a filter was active when a transaction in the transaction history occurred.