3D Virtual Model Product Ranking for E-Commerce
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Solution Overview
Problem
Users face difficulties in finding relevant products in electronic marketplaces due to the need to sift through numerous options, even with search interfaces, as they often lack personalized recommendations based on user preferences and physical characteristics.
Innovation Solution
The system uses a three-dimensional virtual model of the user, generated from image and sensor data, to refine search results by determining a matching score for products based on fit and preferences, ranking items likely to be of interest and displaying them in a visually appealing manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a search interface is provided to enable customers to search for desired products, then customers can find products they are looking for, but customers still have to sift through hundreds or thousands of different products using various options to locate the type of product they are interested in
Solution Approach 1:
The system performs preliminary actions by capturing user biometric data (fingerprints, facial recognition, iris scanning) and analyzing shopping behavior patterns in advance. This pre-processing of user information enables the system to automatically generate personalized product recommendations without requiring users to manually search through products, thus resolving the contradiction between ease of finding products and time consumption.
Solution Approach 2:
The system creates a digital copy or profile of the user based on biometric data and shopping behavior patterns. This user profile serves as a surrogate that automatically guides product recommendations, eliminating the need for users to manually navigate through numerous products. The copied user information is used to pre-filter and rank products, reducing both search time and effort.
2Reliability
If users sift through numerous products to find relevant items, then users can locate specific products, but the process becomes time consuming and potentially frustrating
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring user shopping behavior patterns and using this information to refine and update personalized product recommendations. The system analyzes user interactions with products and adjusts recommendations accordingly, ensuring high accuracy in matching users with relevant products while minimizing the time required to locate items of interest.
3Adaptability or versatility
If the website provides extensive product options and search filters, then users can narrow results by price, ratings, and availability, but users still may not locate items of interest efficiently
Solution Approach 1:
The system performs preliminary filtering and ranking of products based on user biometric profiles and shopping behavior patterns before users even begin their search. This pre-processing adapts the product catalog to each user's preferences, maintaining versatility in product selection while dramatically improving productivity by presenting only the most relevant items first, thus increasing the likelihood of transaction completion.
Data Source
AI summary
Various approaches provide for determining the selection and/or ranking of items to display based at least in part upon the probabilities of a user being interested in those items. The probabilities can be based on profile information of a user. The profile information can include a three-dimensional virtual model of the user. When a search for content is received, the information for the three-dimensional virtual model can be used to determine a set of items. For each item, a matching score quantifying a visual appreciation can be determined based on how well an item virtually “fits” the three-dimensional virtual model. Based on the matching score, the selection and/or ranking of items to display can be determined. In some situations, the items can be shown to appear to be worn by the three-dimensional virtual model. The user can purchase items, cause item information to be presented to other users or sent to other devices, and/or perform other such actions.