Sequence Selection and Extrapolation for Sparse Recommendation Training
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Solution Overview
Problem
Existing sequential recommendation models face challenges in training due to sparse, redundant, or noisy user behavior sequences, leading to inefficiencies in model accuracy and resource utilization.
Innovation Solution
A framework using difficulty and quality (DQ) scores to intelligently sample and extrapolate user behavior sequences, incorporating a meta-extrapolator to generate synthetic sequences for fine-tuning, thereby improving model training efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequential recommendation models are trained with available data, then the model can learn item relationships and user behaviors, but the training accuracy is limited due to data sparsity, redundancy, and noise
Solution Approach 1:
The system employs a self-guided sequence selection mechanism where the model automatically identifies and selects informative sequences based on its own uncertainty estimates. The model computes uncertainty scores for different sequences and autonomously selects which sequences to use for training, eliminating the need for manual data curation and enabling the system to serve itself in identifying high-quality training data.
Solution Approach 2:
The system implements a feedback loop where the model's prediction uncertainty is continuously evaluated and used to guide subsequent data selection. The uncertainty estimates provide feedback about which sequences are most informative for training, allowing the system to iteratively improve by focusing on sequences that maximize learning while minimizing exposure to redundant or noisy data.
2Quantity of substance
If all available sequential recommendation data is used for training, then more data is available for model learning, but the presence of sparse, redundant, and noisy data reduces training efficiency and effectiveness
Solution Approach 1:
The system extracts only the most informative sequences from the available data by computing uncertainty scores and selecting sequences that exceed a certain threshold. This extraction process removes redundant and noisy data from the training set, keeping only the sequences that provide maximum learning value, thereby improving training efficiency without sacrificing data volume.
Solution Approach 2:
The system applies different quality standards to different sequences based on their uncertainty scores. Rather than treating all data uniformly, the system identifies and prioritizes sequences with high information content (high uncertainty) while downweighting or excluding sequences with low information content. This local quality approach ensures that training resources are allocated to the most valuable data portions.
3Measurement precision
If more training data samples are used to improve model accuracy, then better model performance can be achieved, but computational resources and training time increase significantly
Solution Approach 1:
The system applies partial action by selecting only a subset of sequences for training rather than using all available data. By computing uncertainty scores and selecting sequences that provide the most learning value, the system achieves high model accuracy with fewer training samples, thereby reducing training time and computational resource requirements while maintaining or improving model performance.
4Reliability
If sequential recommendation data is filtered to remove noisy and redundant samples, then training data quality improves, but data sparsity increases making training more difficult
Solution Approach 1:
The system uses self-guided selection where the model autonomously identifies informative sequences based on its own uncertainty estimates. This self-service mechanism ensures that the selected sequences are both high quality (reliable) and sufficient in quantity, as the model adapts its selection criteria based on its current knowledge state, preventing excessive data filtering that would lead to sparsity.
Data Source
AI summary
Embodiments described herein provide systems and methods for training a sequential recommendation model. Methods include determining a difficulty and quality (DQ) score associated with user behavior sequences from a training dataset. User behavior sequences are sampled during training based on their DQ scores. A meta-extrapolator may also be trained based on user behavior sequences sampled according to DQ score. The meta-extrapolator may be trained with high quality low difficulty sequences. The meta-extrapolator may then be used with an input of high quality high difficulty sequences to generate synthetic user behavior sequences. The synthetic user behavior sequences may be used to augment the training dataset to fine-tune the sequential recommendation model, while continuing to sample user behavior sequences based on DQ score. As the DQ score is based on current model predictions, DQ scores iteratively update during the training process.