Neural Network Architecture for Metadata-Driven Recommendation
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Solution Overview
Problem
Current recommender systems face challenges in providing personalized item recommendations that effectively incorporate metadata preferences, especially for users with limited consumption history, as they struggle to reflect diverse user tastes and metadata attributes.
Innovation Solution
A computing device employing a neural network architecture that includes a first neural network for reconstructing item consumption information and a second neural network embedded within it, trained on metadata consumption information, to generate item recommendations that reflect metadata preferences by activating specific neural networks based on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network architecture with multiple embedded networks is used to incorporate metadata preferences, then recommendation accuracy is improved, but device complexity increases
Solution Approach 1:
The recommendation system is divided into multiple specialized neural networks, each trained on specific metadata attributes (e.g., genre, director, actor). This segmentation allows each network to focus on particular aspects of user preference, improving overall recommendation accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
Multiple neural networks are embedded within a parent neural network architecture. The child networks process specific metadata types and their outputs are integrated by the parent network to generate final recommendations. This nesting structure enables complex multi-attribute preference modeling while organizing system complexity in a hierarchical manner.
2Adaptability or versatility
If multiple neural networks are embedded to process different metadata types, then personalization capability is improved, but computational resources increase
Solution Approach 1:
The system dynamically activates only the neural networks relevant to the user's input preferences rather than running all networks simultaneously. This dynamic selection reduces computational resource consumption while maintaining high personalization capability by focusing processing power on the most relevant metadata attributes for each recommendation query.
Data Source
AI summary
The disclosure relates to an artificial intelligence (AI) system and an application thereof. A computing device disclosed includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is configured to execute the one or more instructions to input a recommendation recipient's consumption information to a first neural network configured to receive item consumption information of a user and reconstruct the item consumption information and a second neural network embedded into the first neural network and having been trained with respect to metadata consumption information corresponding to the item consumption information, and obtain item recommendation information to which the metadata consumption information is reflected.