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

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

Short answer: Get the source with git clone https://github.com/RUCAIBox/RecBole.git && cd RecBole; then install it with pip install -e . --verbose; run python run_recbole.py to train BPR on ml-100k. All steps.

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  • Data and analytics
  • Beginner
  • 20 minutes
  • From the README
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Who it is forAn ML engineer starting a recommendation project who needs a reference number.

The problemWriting data loading, training and evaluation for each model takes days.

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

What you need first

  • 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

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 for your agent

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.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for RUCAIBox/RecBole and "Run a recommender baseline in under a minute"

Add Deduped to Claude Code once

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

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