AI Recommendation Engine Mapping B2C to B2B Catalogs
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Solution Overview
Problem
Business-to-business (B2B) e-commerce websites lack effective recommendation engines for stocking retail suggestions, unlike business-to-consumer (B2C) sites, leaving corporate buyers with limited inventory management options.
Innovation Solution
An AI-based recommendation engine that maps and filters product recommendations by utilizing machine learning, deep learning, predictive analytics, and natural language processing to align B2C user interests with B2B user purchasing behaviors, even across unrelated product catalogs, through a mapping/filtering module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recommendation engines are implemented on B2B e-commerce websites, then inventory management capability is improved, but system complexity increases due to the need for mapping and filtering modules
Solution Approach 1:
The patent introduces a mapping module as an intermediary that bridges B2C and B2B product catalogs. This mapping module translates B2C product identifiers and attributes into B2B equivalent forms, enabling recommendation transfer without direct integration between the disparate catalog systems. The filtering module acts as another intermediary that selectively passes only relevant recommendations from B2C to B2B contexts, reducing noise and improving precision.
Solution Approach 2:
The recommendation system is segmented into distinct functional modules: a mapping module that handles catalog translation, a filtering module that selects relevant recommendations, and a recommendation generation module that produces final suggestions. This segmentation allows each module to be independently optimized and maintained, reducing overall system complexity while enabling sophisticated inventory management capabilities.
2Quantity of substance
If B2C product recommendations are directly applied to B2B contexts, then recommendation quantity increases, but recommendation precision deteriorates due to catalog mismatches
Solution Approach 1:
The mapping module serves as an intermediary that translates B2C product identifiers and attributes into B2B equivalent forms. This translation process preserves the essence of B2C recommendations while adapting them to B2B catalog structures, maintaining recommendation quantity while improving precision through accurate catalog alignment.
Solution Approach 2:
The system incorporates feedback mechanisms where B2B user interactions with recommendations (acceptance, rejection, modifications) are fed back into the mapping and filtering modules. This feedback continuously refines the mapping relationships and filtering criteria, progressively improving recommendation precision while maintaining adequate quantity through iterative optimization.
Data Source
AI summary
A method of generating digital data content customized for a user of one or more digital data platforms includes determining, through artificial intelligence, interests of a first user of the one or more digital data platforms, as well as those of a plurality of other users. The method further includes generating a product recommendation for the first user by mapping and filtering, e.g., using ontological filtering, natural language processing and/or semantics, the interests determined for the plurality of other users to the interests determined for the first user, and transferring the product recommendation, along with inventory availability, to a client digital data device for presentation to the first user.