Image-Based Shopping Preference Assessment for E-Commerce
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Solution Overview
Problem
Conventional e-commerce search techniques fail to effectively recommend items that align with a user's personal tastes, often providing overwhelming results that are not tailored to the user's preferences, as they rely on user-supplied search terms and do not consider sociological characteristics.
Innovation Solution
An item discovery tool that assesses user shopping preferences through an interactive image-based survey, matching users with others having similar tastes, and generating recommendations based on the preferences of identified 'tastemakers' who frequently interact with the e-commerce platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional e-commerce search techniques are used, then users can search for items using their own terms, but the recommendations do not align with user's personal tastes and provide overwhelming results
Solution Approach 1:
The system performs preliminary preference assessment through an interactive survey before generating recommendations. The survey collects user preferences by presenting images and questions about shopping habits, allowing the system to establish a taste profile in advance. This preliminary action enables more accurate recommendations without requiring complex real-time analysis during the shopping process.
Solution Approach 2:
The patent introduces 'tastemakers' as intermediaries between the system and regular users. Tastemakers are users with established taste profiles who serve as reference points for matching new users. The system matches users with tastemakers having similar preferences and generates recommendations based on the tastemaker's known tastes, simplifying the recommendation process while improving accuracy.
2Measurement precision
If user preferences are assessed through detailed surveys, then recommendation accuracy improves, but the time and effort required from users increases
Solution Approach 1:
The system implements a multi-question survey where users answer a series of questions about their shopping preferences, tastes, and habits. Rather than requiring exhaustive detailed responses, the survey uses multiple moderate questions to collectively build an accurate taste profile. This partial action approach gathers sufficient preference data without demanding excessive user time or effort.
3Adaptability or versatility
If the system matches users with tastemakers having similar tastes, then personalized recommendations improve, but the complexity of user matching algorithms increases
Solution Approach 1:
The system uses feedback from user survey responses to continuously refine and update taste profiles. By collecting user preferences, shopping history, and survey answers, the system generates feedback loops that improve the accuracy of tastemaker matching over time. This feedback mechanism enables personalized recommendations without requiring overly complex algorithms, as the system learns and adapts from user interactions.
Data Source
AI summary
A computer-implemented method includes prompting a first user to select one or more images representing merchandise items, receiving one or more image selections from the first user through the user interface, and determining a shopping preference of the first user based, at least in part, on the image selections. The shopping preference of the first user includes information about the merchandise items represented by the image selections. The method further includes identifying a second user having a shopping preference that is substantially similar to the shopping preference of the first user. The shopping preference of the second user includes information about one or more preferred merchandise items of the second user. The method further includes generating at least one shopping recommendation based on the shopping preference of the second user, and presenting the shopping recommendation to the first user.