Cross-Domain Recommendation Engine Merging User Events
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Solution Overview
Problem
Existing systems for generating product or service recommendations rely solely on user input data from a single domain, leading to less relevant and less confident recommendations when data is scarce, and fail to leverage user input from multiple domains.
Innovation Solution
A system and method that collect and analyze user events across multiple domains to formulate correlations, using a recommendation engine to generate recommendations by interfacing with users via the Internet and storing data in a centralized database, allowing for cross-domain data integration and dynamic updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If collaborative filtering bases recommendations solely on user input data from a single domain, then the system implementation is simple and focused, but the recommendations become less relevant and less confident when data is scarce
Solution Approach 1:
The patent combines user input data from multiple domains (e.g., shopping, news, movies) into a unified collaborative filtering system. By merging data across domains, the system increases the total amount of user feedback available for analysis, thereby improving recommendation quality and confidence even when individual domain data is scarce.
Solution Approach 2:
The system implements a universal recommendation engine that can process and analyze user input data from various domains using the same collaborative filtering algorithms. This multi-functional approach allows the system to leverage data from any domain to generate recommendations, making the system more robust and reliable across different data scenarios.
2Quantity of substance
If the system collects and analyzes user events across multiple domains, then the amount of available data increases improving recommendation quality, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent merges data collection and processing operations across multiple domains into a unified system architecture. By combining user events from different domains through a centralized collaborative filtering framework, the system efficiently handles large volumes of data without requiring separate processing systems for each domain.
Solution Approach 2:
The system uses collaborative filtering to create virtual representations of user preferences across domains. By analyzing patterns in user feedback and creating probabilistic models of user behavior, the system can generate recommendations based on copied patterns from related domains, reducing the need to process every individual data point directly.
3Ease of manufacture
If recommendations are generated based on limited data from a single domain, then the system is easier to implement and maintain, but the recommendations are less relevant and confident
Solution Approach 1:
The patent combines data from multiple domains to compensate for limited data in any single domain. By merging user feedback across domains, the system maintains implementation simplicity while improving recommendation relevance and confidence through increased data volume and diversity.
Data Source
AI summary
A method and system for generating recommendations across multiple product or service domains are disclosed. The system includes a plurality of domain servers for handling user events and for interfacing with users via the Internet, a database for storing the user events, and a recommendation engine. The recommendation engine further includes one or more computer programs containing instructions for collecting the user events across a plurality of product or service domains in the database, receiving a triggering event for recommendations, analyzing the user events to formulate correlations between the user events in the database, and generating recommendations in response to the triggering event in accordance with the correlations between the user events in the database. The disclosure uses user input data from different domains for producing recommendations in any of those domains. The disclosure allows for access to a greater amount of user input data which in turn improves the quality of recommendations.