# AI use cases for Rec Bole (RUCAIBox/RecBole)

Repo: [RUCAIBox/RecBole](https://github.com/RUCAIBox/RecBole) | Use cases: 3 | Page: https://dedupedai.com/use-cases/rucaibox-recbole/

RecBole is a Python and PyTorch library for reproducing and developing recommendation algorithms in one unified framework. It implements 94 recommendation models across general, sequential, context-aware and knowledge-based tasks, ships formatted copies of benchmark datasets, and runs experiments and hyperparameter searches from the run_recbole.py and run_hyper.py scripts.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Run a recommender baseline in under a minute: https://dedupedai.com/use-cases/rucaibox-recbole/benchmark-a-recommender-baseline-with-recbole/
- Compare recommendation models on the same data: https://dedupedai.com/use-cases/rucaibox-recbole/compare-recommendation-models-with-recbole/
- Auto-tune recommender hyperparameters: https://dedupedai.com/use-cases/rucaibox-recbole/tune-recommender-hyperparameters-with-recbole/

## How to run a recommender baseline in under a minute with Rec Bole

Repo: [RUCAIBox/RecBole](https://github.com/RUCAIBox/RecBole) | Difficulty: Beginner | Time: 20 minutes | Basis: From the README

Train and evaluate the BPR model on the ml-100k dataset with one script to see the metrics Rec Bole reports.

**Who it is for:** An ML engineer starting a recommendation project who needs a reference number.

**The problem:** Writing data loading, training and evaluation for each model takes days.

**The outcome:** A trained BPR model with recall@10, mrr@10, ndcg@10, hit@10 and precision@10 on ml-100k.

### Steps

1. Get the source with `git clone https://github.com/RUCAIBox/RecBole.git && cd RecBole`
2. Install it with `pip install -e . --verbose`
3. Run `python run_recbole.py` to train BPR on ml-100k
4. Read the evaluation metrics in the log output

### Prompt to paste into your agent

```text
Using RUCAIBox/RecBole, clone it, install with `pip install -e . --verbose`, run `python run_recbole.py`, and report the final recall@10 and ndcg@10.
```

### Caveat

The README says this example typically takes less than one minute.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/rucaibox-recbole/benchmark-a-recommender-baseline-with-recbole/

## How to compare recommendation models on the same data with Rec Bole

Repo: [RUCAIBox/RecBole](https://github.com/RUCAIBox/RecBole) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Swap the model and key parameters from the command line to compare algorithms under identical evaluation settings.

**Who it is for:** A data scientist choosing between collaborative filtering, sequential and context-aware models.

**The problem:** Comparing models from different codebases mixes up data splits and metrics.

**The outcome:** A table of metrics for several models trained with one framework and one split.

### Steps

1. Install from source as in the README and run the default with `python run_recbole.py`
2. Change the model with `python run_recbole.py --model=[model_name]`, replacing model_name with one from the model list
3. Adjust parameters with `python run_recbole.py --learning_rate=0.0001 --embedding_size=128`
4. Record each run's recall@10 and ndcg@10 and compare

### Prompt to paste into your agent

```text
Using RUCAIBox/RecBole, run `python run_recbole.py --model=[model_name]` for three models of my choice on ml-100k and give me a table of recall@10 and ndcg@10 for each.
```

### Caveat

Larger models and datasets need a GPU; the README links time and memory cost tables per model family.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/rucaibox-recbole/compare-recommendation-models-with-recbole/

## How to auto-tune recommender hyperparameters with Rec Bole

Repo: [RUCAIBox/RecBole](https://github.com/RUCAIBox/RecBole) | Difficulty: Intermediate | Time: 2 hours | Basis: From the README

Describe a search space in hyper.test and let run_hyper.py find the best settings.

**Who it is for:** An engineer who wants a fair, tuned baseline before shipping a model.

**The problem:** Hand tuning learning rate and embedding size is slow and biased.

**The outcome:** The best parameter set found for a model and dataset, with its results.

### Steps

1. Open hyper.test and set ranges, for example learning_rate loguniform -8, 0 and embedding_size choice [64, 96 , 128]
2. Run `python run_hyper.py --model=BPR --dataset=ml-100k --config_files=test.yaml --params_file=hyper.test`
3. Leave out the config file if you have no custom settings, since it is optional
4. Read the best parameters and result from the output

### Prompt to paste into your agent

```text
Using RUCAIBox/RecBole, write a hyper.test search space for <my model>, run run_hyper.py on <my dataset>, and report the best parameters and their score.
```

### Caveat

The README warns that tuning may take a long time to output the best hyperparameters.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/rucaibox-recbole/tune-recommender-hyperparameters-with-recbole/