System, method, and non-transitory machine-readable medium for self-guided sequence selection and extrapolation

Abstract

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.