Learning Model Recommendation Engine for Dynamic Sourcing Events
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Solution Overview
Problem
Current database systems lack an efficient method for generating recommendations based on the frequency and combination of objects in sourcing events, which limits their ability to provide accurate and dynamic recommendations to users or groups.
Innovation Solution
A learning model-based system that updates frequencies of individual and combined objects in sourcing events, allowing for the generation of recommendations by applying these frequencies to determine probability factors for inclusion in future events, and dynamically expanding tables to accommodate new objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a learning model is implemented to track object frequencies and generate recommendations, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The learning model is segmented into distinct functional components: a frequency tracking module that monitors object inclusion in sourcing events, a probability calculation module that computes inclusion probabilities based on tracked frequencies, and a recommendation generation module that produces suggestions. This segmentation allows each component to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-tracking object frequencies and computing probability factors before recommendations are needed. The learning model continuously updates frequency data in the background, so when a recommendation is requested, the computation is already prepared, reducing latency and improving accuracy without proportionally increasing complexity.
2Measurement precision
If the learning model tracks frequencies of all object combinations, then recommendation relevance is improved, but computational resources increase
Solution Approach 1:
Instead of uniformly tracking all possible object combinations across the entire system, the learning model applies local quality by focusing computational resources on tracking frequencies for objects and combinations that are actually observed or have high potential relevance. The frequency tracking is localized to specific sourcing events and user contexts, reducing overall computational burden while maintaining recommendation relevance.
Solution Approach 2:
The system performs partial action by tracking only the necessary frequency data required for effective recommendations, rather than exhaustively monitoring all possible object combinations. The learning model computes probability factors based on a selective subset of tracked frequencies that have demonstrated predictive value, avoiding the computational overhead of complete enumeration while maintaining sufficient recommendation quality.
3Adaptability or versatility
If the system dynamically expands tables to accommodate new objects, then adaptability is improved, but data structure complexity increases
Solution Approach 1:
The learning model employs dynamic data structures that automatically expand and contract based on observed objects in sourcing events. Tables are dynamically created or extended when new objects are encountered, and frequency tracking is automatically adjusted. This dynamic approach allows the system to adapt to new objects without requiring pre-definition of all possible objects, while the automation of table management reduces the perceived complexity for users.
Solution Approach 2:
The system performs self-service by automatically managing the expansion and organization of data tables when new objects are introduced. The learning model autonomously updates frequency tracking structures, creates new table entries as needed, and maintains data integrity without manual intervention. This self-managing capability provides adaptability to new objects while shielding users from the underlying data structure complexity.
Data Source
AI summary
A method may include receiving, at a recommendation engine, a first indication to create a first sourcing event that includes a first object and a second object. The recommendation engine may respond to the first indication by updating a learning model to increment a first frequency of the first object being included in a sourcing event, a second frequency of the second object being included in a sourcing event, and/or a third frequency of the first object and the second object being included simultaneously in a sourcing event. The recommendation engine may receive a second indication to create a second sourcing event. In response to the second indication, the recommendation engine may apply the learning model to generate a recommendation to add, to the second sourcing event, the first object instead of the second object. Related systems and articles of manufacture are also provided.