How to use Rec Bole: what you can do with it

3 use cases

• Run a recommender baseline in under a minute

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

  • REPO RUCAIBox/RecBole
  • Claude Code
  • Codex
  • Cursor
  • Gemini CLI
  • Data and analytics
  • Beginner
  View prompt

  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.

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• Compare recommendation models on the same data

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

  • REPO RUCAIBox/RecBole
  • Claude Code
  • Codex
  • Cursor
  • Gemini CLI
  • Data and analytics
  • Intermediate
  View prompt

  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.

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• Auto-tune recommender hyperparameters

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

  • REPO RUCAIBox/RecBole
  • Claude Code
  • Codex
  • Cursor
  • Gemini CLI
  • Data and analytics
  • Intermediate
  View prompt

  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.

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Quick start with Rec Bole

1. Check you have what it needs: Python 3.7 or later and torch 1.7.0 or later; Install with `pip install recbole` or `conda install -c aibox recbole`, or from source to get the scripts; Optional: CUDA 9.2 or later for GPU training.
2. Open RUCAIBox/RecBole on GitHub and read the README and license.
3. Get the source with git clone https://github.com/RUCAIBox/RecBole.git && cd RecBole
4. Install it with pip install -e . --verbose
5. Run python run_recbole.py to train BPR on ml-100k
6. Read the evaluation metrics in the log output

These steps come from our top use case, Run a recommender baseline in under a minute, which also has a copy-paste agent prompt.

Using the Deduped MCP, get the setup steps for RUCAIBox/RecBole

claude mcp add --transport http deduped https://dedupedai.com/mcp

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About this repo

• Who it is forResearchers and ML engineers who want to benchmark recommender models on the same data and metrics before choosing one.
• Why it is hot4.6k stars, talked about in 2 AI sources we track
• Sourcegithub.com/RUCAIBox/RecBole · Back to Hot repos

Requirements

• Python 3.7 or later and torch 1.7.0 or later
• Install with `pip install recbole` or `conda install -c aibox recbole`, or from source to get the scripts
• Optional: CUDA 9.2 or later for GPU training

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