Hybrid Machine Learning Recommendation Engine
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Solution Overview
Problem
Existing product recommendation systems face challenges in providing accurate predictions when limited data is available, as they rely on content-based filtering, collaborative filtering, and classification methods that have individual drawbacks, such as requiring manual term assignment, large candidate product pools, and ineffective labeling.
Innovation Solution
A method involving unsupervised clustering and supervised learning to generate a recommendation vector, where users are clustered based on past interactions and features, and a rule-based model is created to predict user interest in items, allowing for efficient item recommendation even with sparse data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation systems use content-based filtering or collaborative filtering, then they can provide personalized recommendations, but they require large amounts of data and manual labeling which are ineffective when data is sparse
Solution Approach 1:
The patent changes the fundamental parameters of the recommendation approach by transitioning from supervised learning to unsupervised learning. This allows the system to operate effectively with sparse data by identifying patterns without requiring extensive labeled training data, thereby maintaining recommendation accuracy while reducing data volume requirements
Solution Approach 2:
The patent substitutes manual term assignment and labeling processes with automated unsupervised clustering algorithms. This replacement eliminates the need for manual intervention in data preparation, allowing the system to automatically discover user similarities and item relationships even when data is limited
2Ease of manufacture
If manual term assignment is used in content-based filtering, then product descriptions can be categorized, but it requires significant manual effort and time
Solution Approach 1:
The system performs self-service by automatically generating product categorizations through unsupervised clustering without requiring manual term assignment. The algorithm independently identifies patterns in product data and creates meaningful groupings, eliminating the need for human annotators while maintaining categorization quality
Solution Approach 2:
Manual term assignment is replaced with automated clustering algorithms that perform the categorization function. This substitution transforms a labor-intensive manual process into an automated computational process that operates without human intervention, significantly reducing time loss
3Adaptability or versatility
If collaborative filtering is used with large candidate product pools, then diverse recommendations can be generated, but computational resources increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the large candidate product pool into smaller clusters based on item similarities discovered through unsupervised learning. This segmentation reduces the computational complexity of generating diverse recommendations by organizing items into manageable groups that can be processed more efficiently while maintaining recommendation diversity
Data Source
AI summary
The system and methods of the disclosed subject matter provide a hybrid machine learning approach for recommending items that a consumer should be shown as a next best offer. The recommendation may be based on the consumer's previous behavior, other consumers' previous behavior, and the consumer's profile. The system and methods may cluster an input dataset using an unsupervised clustering engine. The dataset output from the unsupervised clustering engine may be subsequently provided to the input of a supervised machine learning engine to generate a rules-based model. The system and methods may use the rules-based model to subsequently cluster new user data and generate recommendations based on the user's assigned cluster.