GLMM Garment Size Recommendation Model
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Solution Overview
Problem
Conventional methods for recommending garment sizes in e-commerce platforms face challenges such as data sparsity due to seasonal changes in fashion trends and issues like vanity sizing, making it difficult to provide accurate size recommendations, leading to high return rates of clothing items.
Innovation Solution
The implementation of a Generalized Linear Mixed Model (GLMM) that combines collaborative filtering and content-based approaches to determine fit scores by using both fixed and random effect coefficients, allowing for a more robust size recommendation system by modeling local and global fit patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional size recommendation methods are used, then implementation is simple, but recommendation accuracy deteriorates due to data sparsity and seasonal changes
Solution Approach 1:
The patent segments the size recommendation problem into two distinct components: a content-based model that analyzes garment and user attributes, and a collaborative filtering model that leverages user behavior patterns. This segmentation allows each model to specialize in different aspects of the recommendation task, improving overall accuracy while managing complexity through modular design
Solution Approach 2:
The patent merges the content-based model and collaborative filtering model into a unified recommendation system. The content-based model provides recommendations based on attribute matching, while the collaborative filtering model supplements this with patterns from user behavior data. This combination addresses data sparsity by using the content-based model when collaborative data is insufficient, thereby improving recommendation accuracy
2Reliability
If conventional size recommendation methods are used, then computational resources are conserved, but return rates increase due to inaccurate recommendations
Solution Approach 1:
The patent performs preliminary action by pre-processing user profile data and garment attribute data into structured formats before the actual recommendation is made. User profiles are pre-computed with relevant features, and garment data is pre-tagged with size-related attributes. This preliminary preparation reduces computational burden during the recommendation phase while improving the reliability of fit predictions
Solution Approach 2:
The patent replaces traditional mechanical size recommendation systems with an information-based system that uses machine learning models. Instead of relying on simple size charts or manual measurements, the system substitutes computational models that analyze multiple attributes and user behaviors, thereby improving fit prediction reliability while managing computational resources through efficient algorithm design
3Measurement precision
If simple recommendation models are used, then processing speed is fast, but recommendation accuracy deteriorates due to vanity sizing and fashion trends
Solution Approach 1:
The patent applies dynamics by making the recommendation model adaptive to changing fashion trends and user preferences. The collaborative filtering component dynamically updates based on recent user behavior patterns, allowing the system to adapt to seasonal changes and vanity sizing trends. This dynamic approach improves fit score accuracy while the modular structure manages model complexity
Data Source
AI summary
Disclosed are methods, systems, and non-transitory computer-readable medium for generating recommendations regarding products. A method may include determining a set of content features including one or more product attributes; determining a set of latent features; receiving a query user identifier and a query product identifier; determining a feature vector associated with the query user identifier and the query product identifier based on the set of content features and the set of latent features; determining one or more model coefficients for a linear model; and utilizing the linear model to determine a fit score for the query user identifier and the query product identifier.