Machine Learning Offer Recommendation System with De-duplication
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Solution Overview
Problem
Consumers are overwhelmed with irrelevant product and service offers due to the ease and low cost of electronic media distribution, leading to annoyance and a high likelihood of ignoring valuable offers, which negatively impacts providers by reducing customer satisfaction and future sales.
Innovation Solution
A method and system using machine learning to recommend relevant and non-duplicative offers by transforming user data into model training data, identifying attribute matches between users and offers, and de-duplicating irrelevant offers based on user behavior and preferences, ensuring only relevant offers are presented.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If offers are widely distributed via electronic media to maximize reach, then the quantity of offers presented to consumers increases, but the relevance of offers to individual consumers decreases
Solution Approach 1:
The patent segments the broad consumer base into distinct user profiles based on demographics, behavior, and preferences. By dividing the audience into segments, the system can deliver targeted offers to each segment, maintaining high relevance while distributing offers widely across different population groups.
Solution Approach 2:
The system performs preliminary actions by collecting and analyzing user data beforehand to create detailed user profiles and preferences. This preliminary analysis enables the system to pre-determine which offers are relevant to which users before distribution, ensuring relevance is maintained even as offer quantity increases.
2Measurement precision
If machine learning models are trained on extensive user data to improve recommendation accuracy, then the precision of offer matching increases, but the complexity of data processing increases
Solution Approach 1:
The patent segments the complex data processing task into distinct components: data collection, profile creation, preference analysis, and offer matching. By dividing the processing workflow into segments, the system can apply different processing techniques to each segment, improving accuracy while managing complexity through modular processing.
Solution Approach 2:
The system introduces user profiles as an intermediary data structure between raw user data and final offer recommendations. This intermediary layer simplifies the matching process by pre-organizing user characteristics and preferences, reducing the computational complexity of direct data-to-offer matching while maintaining high recommendation accuracy.
Data Source
AI summary
Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.