Dynamic Variant Recommendation System for E-Commerce Catalogs
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Solution Overview
Problem
In electronic marketplaces, the large number of product variants makes it difficult for users to find the specific product they want, leading to a sub-optimal user experience due to the overwhelming number of options.
Innovation Solution
The system aggregates product variants based on attributes such as size, scent, and function, using an attribute hierarchy to guide users through the decision-making process by presenting a recommended product variant at each step, dynamically optimizing results based on sales data, customer profiles, and search queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all product variants are displayed to users, then complete product information is provided, but user experience deteriorates due to overwhelming options and decision fatigue
Solution Approach 1:
The patent extracts and highlights only the most relevant product variants based on customer profiles, search queries, and sales data, rather than displaying all variants. This selective extraction reduces the number of options presented to users while maintaining access to complete product information, thereby resolving the contradiction between information completeness and ease of operation.
Solution Approach 2:
The system dynamically changes the parameter of variant selection by using machine learning models to optimize which variants are recommended based on multiple factors including customer history, search behavior, and sales performance. This parameter optimization allows the system to present a curated subset of variants that balances information completeness with user experience.
2Device complexity
If a fixed recommendation approach is used, then system simplicity is maintained, but adaptability to different customer needs and market conditions deteriorates
Solution Approach 1:
The patent implements dynamic recommendation systems that continuously adapt to changing customer preferences, search patterns, and market conditions. The machine learning models are trained on historical data and updated in real-time, allowing the system to dynamically adjust variant recommendations without requiring complex manual reconfiguration, thus resolving the contradiction between system simplicity and adaptability.
Solution Approach 2:
The system incorporates feedback loops where customer interactions, purchase behavior, and search patterns are continuously monitored and fed back into the machine learning models. This feedback mechanism enables the system to automatically learn and adapt to changing customer needs while maintaining a relatively simple architectural structure, addressing the contradiction between complexity and adaptability.
3Device complexity
If manual variant selection is used, then system simplicity is maintained, but productivity and sales conversion deteriorate due to time-consuming selection processes
Solution Approach 1:
The patent applies preliminary action by pre-processing and analyzing customer data, search queries, and sales patterns before the user makes a selection. Machine learning models pre-calculate and rank the most relevant product variants based on historical data, so that when users search, they immediately see optimized recommendations rather than having to manually evaluate all options, thereby improving productivity without excessive system complexity.
Data Source
AI summary
In various embodiments, when a search query for certain products in an electronic catalog is received, a group of related products that are responsive to the search query may be identified. The product variants may be defined by one or more attributes. Each attribute may have one or more attribute values. The product variants may be first classified according to an attribute. One or more representative classes may be selected from the classes, for example, based on sales data (or other metric) and/or meaningful differentiation between the classes. Then a representative product variant from each class may be selected and returned as search results in response to the search query. In some embodiments, selection of the representative product variant from each class may be based on one or more factors, such as sales data, the particular customer's purchase or browsing history, cost, the search query, availability, among others.