Graph Neural Network Recommendation System Tail Item Representation
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Solution Overview
Problem
Existing machine learning based recommendation systems are susceptible to biases such as popularity bias, leading to unfairness and reduced performance.
Innovation Solution
A computer-implemented method for training a graph neural network based recommendation system, which involves receiving a dataset with user, item, and interaction data, processing it to generate graph representation data, modifying this data to enhance learning for items with fewer interactions, and training graph neural network models based on the modified data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the recommendation system uses standard training methods on imbalanced user-item interaction data, then the model can be trained efficiently, but the representations of items with fewer interactions cannot be learned effectively, leading to popularity bias
Solution Approach 1:
The patent applies local quality by treating head items and tail items differently during training. It uses selective sampling to oversample tail items (items with fewer interactions) and undersample head items (popular items), creating a balanced training subset. This localized adjustment to data distribution enables effective representation learning for tail items while maintaining overall system performance, directly addressing the popularity bias problem.
2Reliability
If the system focuses on learning representations for items with fewer interactions, then fairness improves, but training complexity and computational resources increase
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) pre-training on the full dataset to learn general patterns, and (2) fine-tuning on a selectively sampled balanced subset to improve tail item representations. This segmentation allows the system to manage training complexity by breaking down the challenging task of fair recommendation into manageable stages, reducing computational burden while improving fairness.
Solution Approach 2:
The patent implements partial action by selectively sampling only a portion of the data for the fine-tuning phase rather than processing the entire dataset. By applying selective sampling with specific thresholds for head and tail items, the system performs partial training on a curated subset, which reduces computational complexity while still achieving the goal of improved fairness and tail item representation.
3Productivity
If the dataset follows a heavy-tail or long-tail distribution of user-item interactions, then the system can leverage popular items for better overall performance, but items with less interactions suffer from insufficient data for effective learning
Solution Approach 1:
The patent changes the data distribution parameters by applying selective sampling with configurable thresholds (alpha and beta parameters) to adjust the balance between head and tail items. By modifying these sampling parameters, the system can control the proportion of tail items in the training set, enabling effective learning from items with fewer interactions while maintaining overall recommendation performance. This parameter-based control allows flexible adjustment to optimize for different fairness-performance tradeoffs.
Data Source
AI summary
A method for training a graph neural network based recommendation system. The method includes receiving and processing a dataset to generate graph representation data. The dataset includes user data associated with users, item data associated with items, and user-item interaction data associated with interactions between users and items. Some items have less user-item interactions than some other items. The graph representation data includes data associated with user nodes, data associated with item nodes, and data associated with user-item interaction edges. Some item nodes have less user-item interaction edges than some other item nodes. The method further includes processing the graph representation data to obtain modified graph representation data, which can facilitate learning or determining of representations of at least some of the item nodes with less user-item interactions. The method further includes training the graph neural network based recommendation system based at least partly on the modified graph representation data.