Principle:Recommenders team Recommenders Benchmark Model Training
| Knowledge Sources | |
|---|---|
| Domains | Recommender Systems, Benchmarking, Model Training |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
Standardized model training with timing instrumentation trains each recommendation algorithm through a uniform interface while capturing elapsed training time for performance comparison.
Description
In a multi-algorithm benchmark, each recommendation algorithm has a different training API: some use a .fit(data) interface, others require explicit model construction followed by fitting, and some need hyperparameter preprocessing. The Benchmark Model Training principle standardizes this by providing a train_* function for each algorithm that:
- Accepts a params dictionary and the algorithm-specific data object (produced by the corresponding prepare_training_* function).
- Instantiates the model with the given parameters.
- Trains the model inside a Timer context manager to capture wall-clock training time.
- Returns a (model, Timer) tuple, providing both the trained model and the timing measurement.
This uniform return signature (model, Timer) enables the benchmark loop to:
- Capture training time consistently across all algorithms.
- Pass the trained model directly to the prediction/recommendation functions.
- Compare training performance across fundamentally different algorithm families (matrix factorization, deep learning, graph-based, etc.).
Usage
Use this principle when benchmarking multiple algorithms and you need to compare both model quality and training efficiency. The uniform interface allows a single benchmark loop to train all algorithms without algorithm-specific branching.
Theoretical Basis
Each training function follows the pattern:
train_a(params, data) -> (model_a, timer)
1. model = Algorithm_a(**params) # Instantiate with hyperparameters
2. with Timer() as t: # Start timing
3. model.fit(data) # Train on algorithm-specific data
4. return (model, t) # Return trained model + elapsed time
The Timer context manager records wall-clock elapsed time (timer.interval), providing a consistent measurement unit across all algorithms regardless of whether they run on CPU, GPU, or Spark.
Key design decisions:
- Timing scope includes only the
.fit()call, not data preparation or model instantiation, ensuring that only the actual training computation is measured. - Parameter passing is done via keyword expansion (
**params), allowing each algorithm to accept its own set of hyperparameters without a common interface.