Brand Association Model for Online Concierge Recommendations
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Solution Overview
Problem
In large-scale online systems, it is challenging to monitor user preferences and provide high-quality recommendations due to complex data relationships and abstract concepts like brand loyalty, which conventional systems fail to objectively measure effectively.
Innovation Solution
A brand association model is trained using machine learning techniques, such as pointwise or pairwise methods, to determine customer-brand association levels based on past replacement records, allowing the online concierge system to predict and offer personalized recommendations by identifying candidate alternatives associated with specific brands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems are used to monitor user preferences, then system simplicity is maintained, but measurement precision of brand loyalty and user preferences deteriorates
Solution Approach 1:
The patent replaces conventional rule-based recommendation systems with a machine learning model that automatically learns brand association patterns from historical data. The model substitutes manual preference tracking mechanisms with automated computational analysis, achieving precise brand loyalty measurement through pattern recognition rather than explicit user input tracking.
Solution Approach 2:
The patent introduces a brand association model as an intermediary layer between user behavior data and recommendation outputs. This model acts as a mediator that translates complex user interaction patterns into quantifiable brand loyalty metrics, enabling precise measurement without requiring direct user feedback on brand preferences.
2Reliability
If complex data relationships are analyzed to improve recommendation quality, then recommendation accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary analysis by pre-training the brand association model on historical user data to establish brand loyalty patterns before actual recommendation generation. This advance preparation stores learned associations in the model parameters, enabling rapid real-time recommendations without reprocessing complex data relationships during user interactions.
Solution Approach 2:
The patent creates a simplified representation of complex user-brand relationships by copying essential patterns into the trained machine learning model. The model stores condensed brand association knowledge that can be quickly queried during recommendations, avoiding the need to reanalyze raw complex data relationships for each user interaction.
3Measurement precision
If machine learning models are trained on past replacement records, then brand association measurement accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent extracts specific relevant features from past replacement records that directly indicate brand association, such as replacement frequency, brand consistency, and user choice patterns. By selecting only the most informative features rather than processing all available data, the model achieves accurate brand association measurement with reduced training complexity.
Solution Approach 2:
The patent transforms raw replacement record data into standardized numerical features suitable for machine learning training. By converting qualitative user behaviors into quantitative parameters like brand preference scores and replacement frequencies, the training process becomes more manageable while maintaining measurement precision.
Data Source
AI summary
An online concierge system may receive, from a customer, a selection of an item that is associated with a first brand. The online concierge system may extract features associated with the customer and features associated with the item. The online concierge system may input the extracted features to a machine learning model that is trained to predict a degree of association between the customer and the first brand associated with the item. The online concierge system may identify candidate alternatives for replacing the item. The candidate alternatives may include a first alternative that is associated with the first brand and a second alternative that is associated with a second brand different from the first brand. The online concierge system may select, based on the degree of association between the customer and the first brand, one or more candidate alternatives to be presented to the customer to replace the item.