Apparel Recommendation System Using Multivariate Matrix Models
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Solution Overview
Problem
Online apparel shopping faces challenges in fitting due to variations in customer body measurements and preferences, as traditional sizing methods are inadequate and do not account for individual differences in body shape, fabric type, weather, and personal style.
Innovation Solution
A processor-implemented method and system for recommending user-specific apparel attributes by processing user and apparel data to derive independent and dependent matrices, using a multivariate multi-structure model to learn patterns, and applying these to recommend prioritized apparels based on body dimensions, color preferences, and patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sizing methods (small, medium, large, extra-large) are used, then the system is simple and easy to operate, but the fitting precision and reliability are insufficient due to inadequate account of individual differences in body shape and measurements
Solution Approach 1:
The patent transforms the traditional discrete size parameters (S, M, L, XL) into continuous measurement parameters by capturing multiple body measurements (chest, waist, hips, inseam, etc.) and apparel measurements. This allows for precise calculation of ease values and personalized size recommendations based on actual dimensional data rather than generic size labels.
Solution Approach 2:
The patent segments the sizing problem into multiple independent measurement components (body measurements, apparel measurements, ease calculations for different body parts). By breaking down the overall fit into specific dimensional segments, the system can analyze and optimize each aspect separately, improving overall fitting precision.
2Reliability
If multiple body measurements and user-specific parameters are collected and processed, then the recommendation accuracy and reliability are improved, but the data collection difficulty and system complexity increase
Solution Approach 1:
The patent introduces an intermediary processing layer that includes data collection modules, data processing modules with machine learning models, and recommendation modules. This intermediary structure mediates between raw data collection and final recommendations, systematically handling the complexity of processing multiple measurements and user parameters through structured computational steps.
Solution Approach 2:
The patent implements feedback mechanisms where user responses to recommendations, purchase behavior, and return data are collected and used to refine the machine learning models. This continuous feedback loop improves recommendation reliability over time by learning from actual user outcomes and adjusting the processing algorithms accordingly.
3Measurement precision
If user-specific parameters including body measurements, color preferences, and patterns are processed, then the personalization accuracy is improved, but the data processing time and computational requirements increase
Solution Approach 1:
The patent performs preliminary processing by pre-collecting and storing user profile data including body measurements, color preferences, and pattern preferences in advance. This preliminary action allows the system to have user-specific parameters ready before making recommendations, reducing real-time processing requirements and enabling faster personalized recommendations when users browse apparel.
4Adaptability or versatility
If the system accounts for multiple factors including fabric type, weather conditions, and customer preferences, then the adaptability and recommendation quality are improved, but the complexity of data collection and analysis increases
Solution Approach 1:
The patent creates a universal recommendation framework that handles multiple apparel attributes (size, color, pattern) and multiple influencing factors (body measurements, fabric type, weather, user preferences) through a single integrated machine learning system. This multi-functional approach allows the same core processing architecture to adapt to various recommendation scenarios without requiring separate specialized systems for each factor.
Data Source
AI summary
In online apparel shopping, user finds difficulty in choosing apparel of his/her preferences. This disclosure relates to recommend an apparel specific to a user by estimated attribute values in digital environment. An information associated with the user and apparels is processed to obtain a first parameter and a second parameter and associated data independent matrix and data dependent matrix is derived. The data independent matrix and the data dependent matrix is learned to obtain trained multivariate multi structure model. A significant pattern associated with each user is determined by extracting plurality of images. The derived significant pattern is deployed on k-nearest neighbor model to obtain recommended pattern. The trained multivariate multi structure model and the recommended pattern is applied on state of the user in a real time to estimate the attribute values. The estimated attribute values is mapped with existing apparels in repository to recommend prioritized apparels.