Seasonal Affinity Scoring for Personalized E-Commerce Interfaces
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Solution Overview
Problem
Current systems fail to account for changing user preferences due to seasons, leading to ineffective item selection and presentation in e-commerce environments.
Innovation Solution
A system that determines user-specific seasonal and seasonal theme affinity scores using trained scoring calculation models, incorporating historical transaction data to personalize item selection and presentation in interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current scoring functions use only historical item selection data, then the system maintains simplicity in implementation, but it fails to capture changing user preferences due to seasons
Solution Approach 1:
The patent segments the scoring system into multiple independent components: base affinity scores from historical data, seasonal affinity scores for different time periods, and seasonal theme affinity scores for specific events. Each component is calculated separately and then combined, allowing the system to capture seasonal variations without requiring a complete redesign of the scoring mechanism.
Solution Approach 2:
The patent introduces dynamic seasonal affinity scores that automatically adjust based on the current season and seasonal theme. The scoring system transitions from static historical preferences to dynamic preferences that change with seasons, holidays, and events, enabling the system to adapt to temporal variations in user behavior.
2Measurement precision
If the system incorporates seasonal and seasonal theme affinity scores, then user preference accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies partial action by selectively applying seasonal and seasonal theme affinity scores only when relevant to the current context. Instead of calculating all possible affinity scores for all users at all times, the system computes only the necessary components based on current season and user behavior patterns, reducing unnecessary computational overhead.
Solution Approach 2:
The system performs preliminary calculations by pre-computing seasonal affinity scores and seasonal theme affinity scores based on historical data and known seasonal patterns. These pre-computed scores are stored and readily available when needed, avoiding the need for complex real-time calculations during item recommendation generation.
3Ease of operation
If the system personalizes item selection based on seasonal preferences, then user engagement improves, but the interface complexity increases
Solution Approach 1:
The patent applies local quality by personalizing only specific portions of the interface based on user seasonal preferences. Instead of completely redesigning the interface, the system selectively presents interface elements (such as recommended items, promotional content, or category highlights) that align with the user's current seasonal affinity, maintaining overall interface simplicity while enhancing personalization.
Data Source
AI summary
Systems and methods for generating and using seasonal affinity scores is disclosed. A set of user-specific historical transaction data is obtained and a user-specific affinity score including at least one of a user-specific season affinity score or a user-specific seasonal theme affinity score is determined by determining one or more product affinity scores for a set of product taxonomies and combining the one or more product affinity scores with one or more product index scores to generate the user-specific affinity score. The product affinity scores are determined by a trained scoring calculation model configured to receive the set of user-specific historical transaction data. One or more interface elements are selected based on the user-specific affinity score and an interface is generated including the one or more interface elements.