Contextual Recommendation System Using Nodal Model for Checkout
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Solution Overview
Problem
Consumers often find automated product recommendations at checkout systems irrelevant and overwhelming, leading to avoidance of the checkout process due to the burden of receiving numerous suggestions.
Innovation Solution
A nodal model is used to determine the weighted likelihood of an entity's interest in a second item based on their selection of a first item, with communication characteristics influencing the format of recommendations, which are then encoded and transmitted to the entity for optimized delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated checkout systems provide a plethora of product recommendations to consumers, then the merchant can increase sales through additional purchases, but the consumer experiences burden and fatigue leading to avoidance of the checkout process
Solution Approach 1:
The patent extracts only the most relevant product recommendations from the complete set of possible recommendations, presenting a limited subset to the consumer at checkout. This reduces the burden of processing numerous suggestions while still capturing meaningful cross-selling opportunities, thereby maintaining ease of operation while preserving productivity benefits.
Solution Approach 2:
The recommendation system applies local quality by tailoring the quantity and type of recommendations to specific contexts - different consumers receive different numbers of recommendations based on their purchase history, preferences, and the specific items they are buying. This localized approach ensures recommendations are relevant without being overwhelming, resolving the contradiction between sales potential and user experience.
2Productivity
If automated checkout systems provide numerous product recommendations to consumers, then the merchant can increase sales through additional purchases, but the recommendations become irrelevant and overwhelming to the consumer
Solution Approach 1:
The system incorporates feedback mechanisms that analyze consumer responses to recommendations (purchases, views, ignores) and continuously refine the recommendation algorithm. This feedback loop ensures that recommendations remain relevant and accurate over time, preventing information loss while maintaining productivity by targeting the right consumers with the right products.
Solution Approach 2:
The recommendation system dynamically changes parameters such as the number of recommendations displayed, the types of products suggested, and the timing of recommendations based on consumer behavior patterns. This adaptability ensures recommendations remain relevant and useful, resolving the contradiction between maximizing sales and maintaining recommendation quality.
Data Source
AI summary
Systems and methods for providing recommendations. Input is received that indicates that an entity has selected a first item for purchase. A nodal model is utilized to determine a weighted likelihood that the entity may purchase a second item based on the entity selecting the first item. The weighted likelihood and communication medium is employed to select a format for providing a recommendation to the entity for purchase of the second item. The recommendation is encoded within a message in the format. The message is transmitted to the entity.