README.md

---
tags:
- recommendation
- generative-recommendation
- semantic-id
- diger
- rq-vae
- llama-embeddings
pretty_name: DIGER Processed Data and Embeddings
---

# DIGER Processed Data and Embeddings

This dataset repository contains the processed artifacts used by **DIGER: Differentiable Semantic IDs for Generative Recommendation**.

The files are provided to make reproduction easier, since small differences in preprocessing or embedding generation may lead to different semantic IDs and recommendation results.

## Contents

The repository contains processed files for three separate datasets. These datasets are not mixed together; DIGER trains and evaluates them independently.

- `beauty/`
- `instruments/`
- `yelp/`

Each dataset directory contains:

- `*.train.jsonl`, `*.valid.jsonl`, `*.test.jsonl`: processed interaction splits used by DIGER.
- `*.emb_map.json`: mapping between processed item ids and embedding rows.
- `*.emb-llama.npy`: LLaMA-based item embeddings used for RQ-VAE checkpoint training and DIGER experiments.
- `*_stats.json` when available: summary statistics for the processed split.

## LLaMA Embeddings

The LLaMA embeddings follow the generation procedure described in:

https://github.com/honghuibao2000/letter

We include the processed embeddings here so that downstream users can reproduce the released DIGER artifacts without depending on small preprocessing or embedding-generation differences. Each dataset uses its own embedding matrix and is trained independently.

## Models

The corresponding released RQ-VAE checkpoints are trained separately for each dataset and are available at:

- Beauty: https://huggingface.co/junchenfu/diger-rqvae-beauty
- Instruments: https://huggingface.co/junchenfu/diger-rqvae-instruments
- Yelp: https://huggingface.co/junchenfu/diger-rqvae-yelp

## Source Dataset Note

The underlying recommendation datasets are public datasets from their original sources. This repository hosts the processed DIGER artifacts and embeddings for reproducibility; it is not intended to replace or relicense the original datasets.

Please consult the original dataset sources and their terms before using these files.

## Loading

The split files can be read as JSON Lines. For example:

```python
import json
import numpy as np

with open("beauty/beauty.train.jsonl") as f:
    first = json.loads(next(f))

emb = np.load("beauty/Beauty.emb-llama.npy")
```

You can download files with `huggingface_hub`:

```python
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="junchenfu/diger-processed-data",
    repo_type="dataset",
    filename="beauty/Beauty.emb-llama.npy",
)
```

## Citation

If you use these processed artifacts, please cite the DIGER paper and the original dataset sources.