Core Components Overview

Torch-RecHub is modular: features, data, models, training, and tools are separated for clarity and extensibility.

Architecture

1. Feature layer – dense, sparse, and sequence feature definitions.
2. Data layer – loading, preprocessing, and dataloader generation.
3. Model layer – ranking, matching, multi-task, and generative models.
4. Training layer – unified trainers for fit/eval/predict/ONNX export.
5. Tools layer – ONNX export, visualization, callbacks, losses, etc.

Component Relations

• Feature layer guides preprocessing in the data layer.
• Data generators feed the training layer.
• Models are consumed by trainers.
• Trainers call tools for export/visualization/tracking.

Component Details

• Feature processing: DenseFeature, SparseFeature, SequenceFeature. See Features.
• Data pipeline: TorchDataset, PredictDataset, DataGenerator, MatchDataGenerator. See Data.
• Training & evaluation: CTRTrainer, MatchTrainer, MTLTrainer (and generative trainer variants). See Training & Evaluation.