Complex Entity Recommendation via Bipartite Graph Convolution
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Solution Overview
Problem
Existing recommendation systems face challenges in accurately recommending items due to their simplistic two-entity models, which fail to capture the subtleties and complexities of real-world systems with multiple sources of information, leading to poor accuracy and maintenance issues.
Innovation Solution
The system models both the recipients and items to be recommended as complex entities with static and dynamic components, utilizing bipartite graphs to represent relationships between entities, and employs a multi-layer neural network architecture that includes graph-based convolutional neural networks to compute vector representations and generate relevance scores without the need for hand-designed features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a simplistic two-entity model is used for recommendation, then the system formulation is tractable and can be addressed using rigorous algorithms, but the system ignores subtleties and complexities of key information available in the system, leading to poor accuracy
Solution Approach 1:
The patent segments complex entities into multiple sub-entities (e.g., user segmented into demographics, preferences, behavior patterns; item segmented into attributes, categories, features). This segmentation allows the system to capture fine-grained information and relationships while maintaining tractable formulations through modular processing of each sub-entity and their relationships.
Solution Approach 2:
The patent transitions from a two-entity model to a multi-entity model by adding dimensional complexity. It introduces multiple entity types (users, items, contexts, relationships) and their interconnections, effectively moving from a 2D entity-space to a higher-dimensional entity-relationship space that captures system subtleties and complexities.
2Measurement precision
If content filtering approaches with hand-designed features are used, then the system can capture key information in the system, but designing features is a difficult problem and the methods suffer from poor accuracy and difficult maintenance cycle
Solution Approach 1:
The patent implements self-service by using automated machine learning algorithms and computational models that automatically learn and extract features from raw data without manual intervention. The system self-adjusts to capture key information patterns, eliminating the need for expert feature engineering while maintaining high information capture capability and reducing maintenance burden.
Solution Approach 2:
The patent replaces the mechanical feature design process (manual feature engineering) with an automated computational system. Instead of manually designing and maintaining features, the system uses algorithms to automatically learn representations from data, substituting human cognitive work with computational processes that are more accurate and easier to maintain.
3Measurement precision
If a complex multi-entity model is used to capture information subtleties, then recommendation accuracy improves, but the system complexity increases making it harder to maintain
Solution Approach 1:
The patent creates a universal framework that handles multiple entity types and relationships through a single cohesive model structure. The same computational algorithms and processing mechanisms apply across different entity types (users, items, contexts), providing multi-functionality that simplifies maintenance compared to separate specialized systems for each entity type.
Solution Approach 2:
The patent manages complexity by dynamically adjusting model parameters and processing depth based on specific recommendation scenarios. The system can modify which entity relationships are considered, what level of detail is processed, and which algorithms are applied, allowing high accuracy when needed while reducing computational burden and simplifying maintenance for less complex cases.
Data Source
AI summary
Systems and methods for entity recommendation can make use of rich data by allowing the items to be recommended and the recipients of the recommendation (e.g., users) to be modeled as “complex entities” composed of one or more static sub-entities and/or a dynamic component, and by utilizing information about multiple relationships between the sub-entities as reflected in bipartite graphs. Generating recommendations from such information may involve creating vector representations of the sub-entities based on the bipartite graphs (e.g., using graph-based convolutional networks), and combining these vector representations into representations of the items and users (or other recipients) to be fed into a classifier model.