Principle:Recommenders team Recommenders Benchmark Data Preparation
| Knowledge Sources | |
|---|---|
| Domains | Recommender Systems, Benchmarking, Data Preparation |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
Standardized data preparation converts a common pandas DataFrame into algorithm-specific data formats to enable fair comparison across diverse recommender algorithms.
Description
When benchmarking multiple recommendation algorithms, each algorithm expects its training data in a distinct format. For example, Spark-based algorithms require Spark DataFrames, Surprise-based algorithms require Trainset objects, Cornac algorithms require Cornac Datasets, and deep learning models require their own dataset wrappers (NCFDataset, ImplicitCF, RecoDataLoader). The Benchmark Data Preparation principle establishes a uniform interface: each algorithm has a dedicated prepare_training_* function that accepts the same pandas DataFrames (train and test splits) and returns the algorithm-specific data format needed for training.
This abstraction layer ensures that:
- All algorithms start from the same source data (a pandas DataFrame with userID, itemID, rating, and timestamp columns).
- Data format conversion is encapsulated per algorithm, keeping the benchmark loop clean.
- Fair comparison is maintained because every algorithm receives the same underlying data, just in different structural representations.
Usage
Use this principle whenever you need to benchmark or compare multiple recommendation algorithms that expect different data formats. The prepare_training_* functions serve as adapters between the common pandas DataFrame and each algorithm's native input type.
Theoretical Basis
The adapter pattern from software engineering is applied here: a common interface wraps heterogeneous data format requirements. Given a training set as a pandas DataFrame with columns (userID, itemID, rating, timestamp), each preparation function for algorithm transforms it:
f_a : pd.DataFrame -> T_a
where T_a is the algorithm-specific type:
T_sar = pd.DataFrame (identity, no conversion)
T_als = pyspark.sql.DataFrame (Spark schema with typed columns)
T_svd = surprise.Trainset (Surprise internal format)
T_ncf = NCFDataset (CSV-backed dataset with user/item mappings)
T_cornac = cornac.data.Dataset (UIR triplets for BPR and BiVAE)
T_embdotbias = RecoDataLoader (string-typed user/item with validation split)
T_lightgcn = ImplicitCF (implicit feedback graph structure)
The key invariant is that all accept the same input signature (train: pd.DataFrame, test: pd.DataFrame) and produce data suitable for the corresponding train_a function.