Seasonal Recommendation System Using Theme-Aware Filtering
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Solution Overview
Problem
Ecommerce platforms face challenges in effectively recommending items based on seasonal trends, as existing systems often rely on historical customer data and do not accurately capture peak purchase times or relevant product types for specific seasons or events, leading to irrelevant item displays.
Innovation Solution
A seasonal recommendation system that computes a seasonality index score for product types using historical data and applies a theme-aware model to filter out non-seasonal items, ensuring relevant items are displayed based on peak transaction periods and thematic relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If historical customer data is used for recommendations, then system complexity is reduced, but recommendation accuracy for seasonal items deteriorates
Solution Approach 1:
The patent segments the recommendation system into multiple components: a seasonal recommendation module that identifies peak purchase times and thematic relevance, and a traditional recommendation module that uses historical customer data. Each module operates independently and their results are combined, allowing the system to benefit from both simple historical data processing and sophisticated seasonal analysis without excessive complexity
Solution Approach 2:
The patent introduces a seasonal recommendation module as an intermediary layer between the user and the recommendation system. This module computes seasonality indices and identifies thematic relevance, then integrates these insights with traditional historical data-based recommendations, improving overall accuracy without requiring complete system redesign
2Measurement precision
If seasonal analysis is performed for all product types, then recommendation relevance is improved, but computational time increases
Solution Approach 1:
The patent applies seasonal analysis selectively rather than uniformly across all product types. The seasonal recommendation module identifies and focuses computational resources on product types that exhibit seasonal patterns and thematic relevance, while skipping products that don't show seasonal behavior, thereby reducing unnecessary computational overhead
Solution Approach 2:
The system performs seasonal analysis on a subset of product types that are most likely to exhibit seasonal patterns, rather than analyzing every single product type. This partial action approach maintains high recommendation relevance for seasonal items while avoiding the computational burden of processing the entire product catalog
3Measurement precision
If theme-aware filtering is applied, then item relevance is improved, but device complexity increases
Solution Approach 1:
The patent pre-computes seasonality indices and thematic models during off-peak periods or in advance of seasonal events. By performing these complex filtering operations beforehand, the system reduces the computational burden during real-time recommendation generation, maintaining high item relevance while managing device complexity through advance preparation
Solution Approach 2:
The theme-aware filtering mechanism is designed to be dynamic and adaptive. The system adjusts the level of filtering applied based on the detected seasonality strength and thematic relevance of different product types, applying more complex filtering only where necessary and using simpler methods where appropriate, thus balancing relevance and complexity
Data Source
AI summary
A seasonal recommendation system can include a computing device that is configured to receive a request to identify a set of recommendations associated with a season, obtain historical data over a threshold period for a set of product types, and compute a seasonality index score based on the historical data over a target period and the threshold period. The computing device is also configured to select a subset of product types based on the seasonality index score and by applying a theme-aware model to the product types and identify and store a set of items corresponding to at least one product type of the subset of product types. The computing device is configured to, in response to a user navigating to a webpage using a user device, select and display at least one item of the set of items on a user interface of the user device.