Cross-Domain Recommendation System for Niche Entity Discovery
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Solution Overview
Problem
Existing recommendation systems fail to provide user-specific recommendations across domains, often neglecting smaller or lesser-known entities and not considering user behavior, purchase history, or donation history, leading to irrelevant suggestions.
Innovation Solution
A system utilizing cross-domain collaborative filtering and ensemble models that incorporate supervised and unsupervised learning to generate personalized recommendations by analyzing user information, transaction history, and peer-to-peer associations, ensuring that recommendations are tailored to individual user interests and behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If popular items or trends are used for recommendations, then the recommendation system is simple to implement, but the recommendations are not tailored to user interests and behaviors
Solution Approach 1:
The patent segments the recommendation system into multiple independent components: user profile module, transaction history module, peer-to-peer association module, and recommendation generation module. Each component processes specific data types independently, allowing the system to maintain simplicity while achieving personalized recommendations through modular architecture.
Solution Approach 2:
The patent transitions from single-dimension popular item recommendations to multi-dimensional personalized recommendations by incorporating user demographics, transaction history, peer associations, and item attributes. This dimensional expansion enables tailored recommendations without significantly increasing system complexity.
2Adaptability or versatility
If user-specific data analysis is implemented, then recommendations are tailored to individual users, but the system complexity increases
Solution Approach 1:
The patent implements a universal recommendation engine that handles multiple data types (user profiles, transactions, peer associations) through a single integrated algorithm. This multi-functional approach personalizes recommendations across different domains while maintaining a unified system architecture that prevents exponential complexity growth.
Solution Approach 2:
The system automatically collects and processes user data without requiring manual intervention. User profiles, transaction histories, and peer associations are gathered and analyzed autonomously, reducing operational complexity while enabling sophisticated personalization.
3Adaptability or versatility
If cross-domain filtering is used, then smaller and lesser-known entities are promoted, but computational resources increase
Solution Approach 1:
The patent applies partial cross-domain filtering by focusing on the most relevant domains and user attributes rather than analyzing all possible data. This selective approach promotes smaller and lesser-known entities to diverse users while consuming manageable computational resources by avoiding exhaustive analysis.
Data Source
AI summary
Aspects of the present disclosure involve systems, methods, devices, and the like for presenting a recommendation. In one embodiment, a system is introduced that includes a plurality of models for obtaining a recommendation score. The recommendation score may be obtained using one or more recommendation models and a recommendation made based on the recommendation score determined. In another embodiment, the system is introduced that can re-train the recommendation model based on a feedback received in response to a recommendation made using on the recommendation score obtained.