Recommendation System Feature Selection Feedback Loop
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Solution Overview
Problem
Existing recommendation systems face challenges in effectively selecting appropriate items from large inventories due to overwhelming data, leading to poor performance, as they struggle to analyze both structured and unstructured data and often recommend inappropriate or poorly matched items.
Innovation Solution
A system that utilizes processors to analyze client and item data, employing multiple recommendation processes and feature selection processes to identify relevant subsets of items, combined with a feedback interface to update and refine feature selection based on client feedback, thereby improving system performance over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all client data and item inventory data are presented to entities for item selection, then complete information is available for decision-making, but the entities become overwhelmed with data leading to poor recommendation system performance
Solution Approach 1:
The patent segments the large dataset into multiple subsets, each generated by different recommendation processes. Instead of presenting all data at once, the system divides client data and item inventory data into manageable portions that can be processed separately and then integrated, preventing entity overload while maintaining information completeness.
Solution Approach 2:
The feature selection process extracts only the most relevant features from the comprehensive dataset before presenting them to entities. This extraction mechanism filters out unnecessary information while retaining critical data points, ensuring entities receive concise, actionable information rather than overwhelming raw data.
2Reliability
If multiple recommendation processes are executed to improve item selection accuracy, then recommendation quality improves, but system complexity and processing time increase
Solution Approach 1:
The patent merges multiple recommendation processes into a unified system where different algorithms work together. Each process contributes to generating item subsets, and their results are combined through feature selection and entity review, achieving high accuracy while managing complexity through integrated architecture.
Solution Approach 2:
The system executes multiple recommendation processes but only presents a selected subset of features and items to entities for final decision-making. This partial action approach ensures thorough analysis through multiple processes while avoiding the complexity burden of presenting all generated information.
3Ease of operation
If feature selection processes are used to reduce data presentation to entities, then entity workload is reduced, but risk of losing important information increases
Solution Approach 1:
The feedback interface allows entities to provide input on the usefulness of presented features and items. This feedback loop enables the system to learn from entity decisions and adjust future feature selection, ensuring that reduced data presentation does not compromise information quality but rather optimizes it based on actual usage patterns.
Solution Approach 2:
The feature selection process performs preliminary analysis and filtering before data is presented to entities. By pre-processing the data to identify and highlight the most relevant features, the system reduces entity workload while ensuring that critical information is not lost but rather prioritized for effective decision-making.
Data Source
AI summary
Data about clients, items included in an item inventory, executable code for recommendation processes, and executable code for feature selection process(es), are stored. The recommendation processes are used to identify items in the item inventory that are recommended for specific clients, and the feature selection process(es) is/are used to select features that are displayed to entities tasked with selecting items from the item inventory for clients. Selection feedback and client feedback are received. One or more feature selection process(es) is/are modified based on the selection feedback and/or the client feedback, to thereby change which, where or/or how certain portion of the data about clients is displayed to the entities tasked with selecting items from the item inventory for the clients, and/or change which, where and/or how certain portion of the data about items is displayed to the entities tasked with selecting items from the item inventory for the clients.