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How to auto-tune recommender hyperparameters with Rec Bole

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

Short answer: Open hyper.test and set ranges, for example learning_rate loguniform -8, 0 and embedding_size choice [64, 96 , 128]; then run python run_hyper.py --model=BPR --dataset=ml-100k --config_files=test.yaml --params_file=hyper.test; Leave out the config file if you have no custom settings, since it is optional. All steps.

  • Data and analytics
  • Intermediate
  • 2 hours
  • From the README
  • Claude Code
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  • Gemini CLI

Who it is forAn engineer who wants a fair, tuned baseline before shipping a model.

The problemHand tuning learning rate and embedding size is slow and biased.

The outcomeThe best parameter set found for a model and dataset, with its results.

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. 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 for your agent

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.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for RUCAIBox/RecBole and "Auto-tune recommender hyperparameters"

Add Deduped to Claude Code once

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

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