Datasets:

AL-GR
/

Item-EMB

language:
  - en
  - zh
license: apache-2.0
pretty_name: AL-GR Item Embeddings
tags:
  - multimodal
  - embedding
  - computer-vision
  - recommendation
  - e-commerce
task_categories:
  - feature-extraction
  - image-feature-extraction
dataset_info:
  - config_name: default
    splits:
      - name: train
        num_examples: 507000000

AL-GR/Item-EMB: Multi-modal Item Embeddings

Dataset Summary

This repository, AL-GR/Item-EMB, is a companion dataset to the main AL-GR generative recommendation dataset. It contains the 512-dimensional multi-modal embeddings for over 500 million items that appear in the AL-GR sequences.

Each item is represented by a unique ID (base62_string) and its corresponding vector embedding. To ensure compatibility with text-based formats like CSV, the float32 vectors have been encoded into a Base64 string.

This dataset allows users to:

Initialize item embedding layers in traditional or multi-modal recommendation models.
Analyze the semantic space of items (e.g., through clustering or visualization).
Link the abstract semantic IDs from the AL-GR dataset to their rich, underlying vector representations.

How to Use

The core task when using this dataset is to decode the feature string back into a NumPy vector. Below is a complete example of how to load the data and perform the decoding.

import base64
import numpy as np
from datasets import load_dataset

def decode_embedding(base64_string: str) -> np.ndarray:
    """Decodes a Base64 string into a 512-dimensional numpy vector."""
    # Decode from Base64, interpret as a buffer of float32, and reshape.
    return np.frombuffer(
        base64.b64decode(base64_string),
        dtype=np.float32
    ).reshape(-1)

# 1. Load the dataset from the Hugging Face Hub
# NOTE: Replace [your-username] with the actual username
dataset = load_dataset("AL-GR/Item-EMB")

# 2. Get a sample from the dataset
sample = dataset['train'][0]
item_id = sample['base62_string']
encoded_feature = sample['feature']

print(f"Item ID: {item_id}")
print(f"Encoded Feature (first 50 chars): {encoded_feature[:50]}...")

# 3. Decode the feature string into a vector
embedding_vector = decode_embedding(encoded_feature)

# 4. Verify the result
print(f"Decoded Vector Shape: {embedding_vector.shape}")
print(f"Decoded Vector Dtype: {embedding_vector.dtype}")
print(f"First 5 elements of the vector: {embedding_vector[:5]}")

# Expected output:
# Item ID: OvgEI
# Encoded Feature (first 50 chars): BHP0ugrXIz3gLZC8bjQAVwnjCD3g1t27FCLgvF66yT14C6S9Aw...
# Decoded Vector Shape: (512,)
# Decoded Vector Dtype: float32
# First 5 elements of the vector: [ ...numpy array values... ]

Dataset Structure

Data Fields

base62_string (string): A unique identifier for the item. This ID corresponds to the semantic item IDs used in the AL-GR generative recommendation dataset.
feature (string): The Base64 encoded string representation of the item's 512-dimensional multi-modal embedding.

Data Splits

Split	Number of Samples
`train`	~507,000,000

Citation

If you use this dataset in your research, please cite:

License

This dataset is licensed under the Apache License 2.0.