Vector-Based Product Recommendation System Using Value Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing shopping paradigms face inefficiencies and limitations in ensuring that consumers are presented with products best suited to their preferences, as existing preference-based approaches are product-specific and do not effectively assess a wide variety of product and service categories, leading to suboptimal customer experiences.
Innovation Solution
The use of vector-based characterizations of products and partialities, where partiality vectors represent a person's beliefs and preferences, allowing for a comprehensive assessment of product alignment with individual values, affinities, and aspirations, facilitating improved product recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If preference-based approaches are used to select products for consumers, then product relevance to consumer preferences is improved, but the scope and versatility of product categories that can be assessed is limited
Solution Approach 1:
The patent applies universality by creating a unified value vector framework that can assess multiple product categories (electronics, clothing, groceries, services) through a single system. The value vectors represent universal consumer preferences (quality, price, convenience, sustainability) that apply across all product types, allowing the recommendation system to function universally across diverse categories rather than requiring separate preference models for each category.
2Measurement precision
If detailed product-specific preference information is collected, then recommendation accuracy for specific products is improved, but memory requirements and data processing complexity increase
Solution Approach 1:
The patent extracts the essential preference information by representing consumer preferences as compact value vectors with defined dimensions (quality, price, convenience, sustainability) rather than storing detailed product-specific preference data. This extraction reduces memory requirements by capturing only the essential preference characteristics needed for recommendations across all product categories, eliminating the need to store extensive product-specific preference profiles.
3Adaptability or versatility
If comprehensive product assessment across multiple categories is performed, then recommendation versatility is improved, but computational complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming complex product assessments into standardized value vector comparisons. Instead of performing complex computations across diverse product attributes, the system represents both consumer preferences and product characteristics as vectors with consistent dimensions, enabling efficient computational operations (dot products, distance calculations) that scale across multiple product categories without proportionally increasing complexity.
Data Source
AI summary
Systems, apparatuses, and methods are provided herein for content-based product recommendations. A system for content-based product recommendations comprises a content monitoring device configured to monitor video content viewed by a user, a customer vectors database, a product vectors database; and a control circuit being configured to: detect, via the content monitoring device, a video content being viewed by the user, identify an item associated with a current segment of the video content viewed by the user, determine a product category associated with the item, determine alignments between the customer value vectors and the product characteristic vectors for each of the plurality of products in the product category, select a recommended product from the plurality of products based on the alignments between the customer value vectors and the product characteristic vectors for each of the plurality of products, and initiate an offer of the recommended product to the customer.