Multi-modal Graph Neural Network for Video Recommendation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional video recommendation systems face challenges in handling sparse interactions and multi-modal data, leading to inefficient recommendations due to their inability to process different types of information effectively, such as textual, visual, and acoustic data.
Innovation Solution
A multi-modal graph encoder is used to generate feature embeddings for users and content items based on a knowledge graph, incorporating spatial and edge encoding matrices, and trained using contrastive learning and ranking losses to improve recommendation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional neural networks are used for video recommendation, then the system can process user interactions, but it fails to effectively handle sparse interactions and multi-modal data
Solution Approach 1:
The patent segments multi-modal data into distinct modalities (textual, visual, acoustic) and processes each through separate encoding pathways before integration. The graph neural network divides the recommendation task into relationship modeling (knowledge graph) and feature embedding (multi-modal encoder) components, allowing specialized handling of sparse interactions and diverse data types while maintaining overall system reliability
Solution Approach 2:
The patent creates a composite recommendation system that integrates knowledge graph structures with multi-modal neural network encoders. This composite architecture combines the relationship-reasoning strength of knowledge graphs with the feature-extraction capability of multi-modal networks, enabling reliable recommendations despite sparse interactions across multiple data modalities
2Quantity of substance
If conventional recommendation systems process large data sizes, then they can cover more content, but interactions become sparse and recommendation efficiency decreases
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary structure that models explicit relationships between users and content items. This intermediary layer captures interaction patterns more efficiently than raw data processing alone, enabling the system to maintain high recommendation efficiency even when covering large quantities of content with sparse user interactions
Solution Approach 2:
The patent transforms the recommendation problem from direct user-item matching into a multi-dimensional space that includes relationship paths in the knowledge graph and multi-modal feature embeddings. This dimensional expansion allows the system to efficiently navigate sparse interaction data across large content catalogs by leveraging additional relational and modal dimensions
3Adaptability or versatility
If the system integrates multiple modalities for recommendation, then it can process different types of information, but the system complexity increases
Solution Approach 1:
The patent employs a universal multi-modal graph encoder that handles multiple data modalities (textual, visual, acoustic) through a unified architecture. This single encoder performs multiple functions including feature extraction, relationship modeling, and embedding generation across all modalities, reducing overall system complexity compared to separate specialized processors for each modality
Solution Approach 2:
The patent merges the knowledge graph processing function with the multi-modal encoding function into an integrated graph neural network architecture. By combining relationship modeling and feature embedding in a single unified system, the patent reduces architectural complexity while maintaining the ability to process diverse multi-modal information effectively
Data Source
AI summary
Systems and methods for item recommendation are described. Embodiments of the present disclosure receive input indicating a relationship between a user and a first content item; generate a knowledge graph based on the input, wherein the knowledge graph comprises relationship information between the user and a plurality of content items; generate a first feature embedding representing the user and a second feature embedding representing a second content item of the plurality of content items based on the knowledge graph, wherein the second feature embedding is generated using a first modality for a query vector of an attention mechanism and a second modality for a key vector and a value vector of the attention mechanism; compare the first feature embedding to the second feature embedding to obtain a similarity score; and recommend the second content item for the user based on the similarity score.