Compositional Recommender Framework Modular Functions
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Solution Overview
Problem
Existing recommender systems in cloud computing are expensive and complex, particularly those using clustering technologies, which require specialized expertise and are slow, while database lookup technologies are fast but time-consuming to establish and require specific experts.
Innovation Solution
A compositional recommender framework that modularizes recommendation functions, allowing them to be daisy-chained and reconfigured, enabling the creation of customized recommendation engines without delving into low-level computer code, by combining clustering and database lookup functions to provide efficient and effective recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clustering technologies are used for recommendations, then recommendation quality and cleverness are improved, but system complexity and cost increase
Solution Approach 1:
The patent divides the recommendation system into separate modular components: clustering functions, database lookup functions, and recommendation generation functions. Each module performs a specific task and can be independently configured, allowing the system to leverage clustering for quality recommendations while managing complexity through modular architecture.
Solution Approach 2:
The patent creates a universal framework that can incorporate multiple recommendation approaches (clustering, database lookups, and other functions) within a single system. This multi-functional architecture allows organizations to benefit from clustering's sophisticated recommendations without requiring the entire system to be complex, as different functions can be activated based on needs.
2Measurement precision
If clustering algorithms are used, then recommendation effectiveness is improved, but processing speed decreases
Solution Approach 1:
The patent performs clustering operations in advance to pre-compute and store cluster information. When recommendations are needed, the system retrieves pre-computed clustering results rather than performing complex clustering algorithms in real-time, thus maintaining recommendation effectiveness while improving processing speed.
Solution Approach 2:
The system separates computationally intensive clustering operations from real-time recommendation generation. Clustering is performed as a batch process in advance, while the recommendation engine uses the pre-computed results for fast online queries, dividing the workload between offline and online processing.
3Measurement precision
If proprietary recommender systems are built, then recommendation capability is improved, but development cost and time increase
Solution Approach 1:
The patent provides a universal recommendation framework that can be applied across different applications and industries. Rather than building custom proprietary systems from scratch, organizations can configure the framework with their own data and parameters, reducing development costs while maintaining recommendation capability.
Solution Approach 2:
The patent enables organizations to replicate successful recommendation patterns and configurations from the framework without reinventing the wheel. Pre-built clustering functions and recommendation templates can be copied and adapted to specific use cases, reducing development time and cost while maintaining effectiveness.
4Reliability
If specialized consultants are hired for clustering, then system performance is improved, but operational cost increases
Solution Approach 1:
The patent designs the recommendation framework to be self-configuring and easy to use, reducing the need for specialized clustering consultants. The system provides automated configuration options and user-friendly interfaces that allow non-experts to set up and maintain effective recommendation systems, thereby reducing operational costs while maintaining performance.
Data Source
AI summary
A compositional recommender framework using modular recommendation functions is described. Each modular recommendation function can use a discrete technology, such as using clustering, a database lookup, or other means. A first recommendation function can recommend to a user items, such as books to check out, automobiles to purchase, people to date, etc. Another modular recommendation function can be daisy chained with the first to recommend items that are similar or related to the first recommended items, such as users who have also checked out the same recommended book, trailers that can be towed by the recommended automobiles, or vacations booked by people that were recommended as people to date. The modular recommendation functions can be used to build customized recommendation engines for different industries.