Meta-Learning Recommender System for Small Shop Bias
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Solution Overview
Problem
Recommender systems for e-commerce platforms suffer from sample selection bias, resulting in poor performance for small shops due to insufficient training data, leading to lower prediction accuracy for new items.
Innovation Solution
A meta-learning process is employed to retrain the machine-learning recommendation model by creating shop-specific versions for both small and large shops, using regularizer terms to adjust parameters and counteract bias, ensuring better recommendation accuracy for small shops while maintaining performance for larger ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning recommendation model is trained using standard methods on e-commerce platform data, then the model achieves good prediction accuracy for large shops with abundant training data, but the model performs poorly for small shops with insufficient training data due to sample selection bias
Solution Approach 1:
The patent applies local quality by creating shop-specific versions of the recommendation model tailored to different shop sizes. For small shops, a specialized training process with modified loss functions is applied, while large shops use the standard training approach. This allows the system to optimize performance locally for each shop size category rather than using a one-size-fits-all approach.
Solution Approach 2:
The patent changes key parameters in the training process for small shops, specifically modifying the loss function to include additional terms that account for sample selection bias. The training data sampling strategy is also changed to oversample small shops, altering the data distribution parameters to ensure adequate representation of underrepresented shop sizes.
2Productivity
If the recommendation model is trained with more data from large shops, then the model performance improves for large shops, but the sample selection bias increases and performance for small shops deteriorates further
Solution Approach 1:
The patent applies partial action by selectively applying different training strategies to different shop size categories. Instead of uniformly processing all shops, the system identifies small shops as a specific subset requiring special handling and applies modified training procedures only to them, while maintaining standard training for large shops. This targeted approach prevents the exacerbation of sample selection bias.
Solution Approach 2:
The patent implements preliminary anti-action by proactively counteracting sample selection bias through modified loss functions that include regularization terms specifically designed to compensate for the underrepresentation of small shops. This anti-bias mechanism is built into the training process before deployment, preventing the model from developing strong biases against small shops.
3Device complexity
If a global recommendation model is used for all shops, then the system maintains simplicity and ease of deployment, but the model cannot adequately address the specific needs and data characteristics of small shops
Solution Approach 1:
The patent segments the recommendation model into shop-specific versions based on shop size categories. The system divides the user base into segments (small shops vs. large shops) and trains separate model instances or applies different configuration parameters for each segment. This segmentation allows tailored optimization for each group while maintaining a unified overall system architecture.
Solution Approach 2:
The patent maintains universality by using a common base model architecture that can serve all shops, while allowing for shop-specific adaptations through configurable parameters and optional specialized training. The system is designed to be multi-functional, capable of operating in both standard mode for large shops and specialized mode for small shops, providing flexibility without requiring completely separate systems.
Data Source
AI summary
The present disclosure relates to improving recommendations for small shops on an ecommerce platform while maintaining accuracy for larger shops. The improvement is achieved by retraining a machine-learning recommendation model to reduce sample selection bias using a meta-learning process. The retraining process comprises identifying a sample subset of shops on the ecommerce platform, and then creating shop-specific versions of the recommendation model for each of the shops in the subset. A global parameter adjustment is calculated for the global model based on minimizing losses associated with the shop-specific models and increasing the probability of items being recommended from small shops. The latter is achieved by introducing regularizer terms for small shops during the meta-learning process. The regularizer terms serve to increase the probability that an item from a small shop will be recommended, thereby countering the sample selection bias faced by small-shop items.