Cognitive Deep Constrained Filtering for Sparse Data Recommendations
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Solution Overview
Problem
Conventional recommendation systems face challenges such as overwhelming product variety, difficulty in generating real-time recommendations, sparse data issues, and inability to leverage attribute importance and fast-moving trends, particularly in industries like fashion, leading to poor performance and cold start problems.
Innovation Solution
A cognitive deep constrained filtering technique that uses customer and product similarity matrices to provide personalized recommendations in real-time, leveraging sparse data and precomputed user features, while constraining recommendations within a specified candidate set based on user queries, using online matrix manipulations and deep neural networks to handle sparse datasets and dynamic constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional recommendation systems process overwhelming product variety, then they can cover more products, but they face difficulty in generating real-time recommendations and suffer from computational complexity
Solution Approach 1:
The patent segments the recommendation process into two distinct phases: offline precomputation phase where customer similarity matrices and product feature matrices are built in advance, and online recommendation phase where only fast matrix manipulations are performed. This segmentation allows comprehensive product variety coverage during offline processing while achieving real-time performance during online recommendations.
Solution Approach 2:
The system performs preliminary actions by precomputing customer similarity matrices, product feature matrices, and candidate sets during offline processing. These precomputed structures are then reused during online recommendations, eliminating the need for complex real-time calculations and enabling fast response times even with overwhelming product variety.
2Quantity of substance
If conventional recommendation systems use sparse data, then they can work with limited user interactions, but they suffer from poor performance and cold start problems
Solution Approach 1:
The patent introduces product feature matrices as intermediary structures that capture product attributes and characteristics independently of user interactions. These feature matrices serve as mediators that enable meaningful recommendations even when user data is sparse, by leveraging product-level information rather than relying solely on user behavior data.
Solution Approach 2:
The system changes the parameters used for recommendations from purely interaction-based metrics to include precomputed product features and attributes. This parameter transformation allows the system to generate reliable recommendations for new users and products (cold start scenarios) by utilizing product feature information rather than requiring extensive interaction data.
3Adaptability or versatility
If conventional recommendation systems process all products, then they can provide comprehensive recommendations, but they cannot effectively leverage attribute importance and fast-moving trends
Solution Approach 1:
The patent extracts and isolates important product attributes and features into dedicated feature matrices during offline processing. By separating key attributes from the full product catalog, the system can efficiently detect and measure attribute importance without processing all products in real-time, while still providing comprehensive recommendations through the structured feature representations.
Data Source
AI summary
A system and method for modeling preferences of customers to drive personalized contextual recommendations using cognitive deep constrained filtering includes receiving a user query on an online retail platform, in response to receiving the user query, performing a first online matrix manipulation and a second online matrix manipulation, and sending a list of ranked recommended products.