Yambda-5B — A Large-Scale Multi-modal Dataset for Ranking And Retrieval

Industrial-scale music recommendation dataset with organic/recommendation interactions and audio embeddings

📌 Overview • 🔑 Key Features • 📊 Statistics • 📝 Format • 🏆 Benchmark • ⬇️ Download • ❓ FAQ

Overview

The Yambda-5B dataset is a large-scale open database comprising 4.79 billion user-item interactions collected from 1 million users and spanning 9.39 million tracks. The dataset includes both implicit feedback, such as listening events, and explicit feedback, in the form of likes and dislikes. Additionally, it provides distinctive markers for organic versus recommendation-driven interactions, along with precomputed audio embeddings to facilitate content-aware recommendation systems.

The Yambda dataset is published exclusively for scientific and research purposes.

Preprint: https://arxiv.org/abs/2505.22238

License

Apache 2.0

Key Features

• 🎵 4.79B user-music interactions (listens, likes, dislikes, unlikes, undislikes)
• 🕒 Timestamps with global temporal ordering
• 🔊 Audio embeddings for 7.72M tracks
• 💡 Organic and recommendation-driven interactions
• 📈 Multiple dataset scales (50M, 500M, 5B interactions)
• 🧪 Standardized evaluation protocol with baseline benchmarks

About Dataset

Statistics

User History Length Distribution

Item Interaction Count

Data Format

File Descriptions

Common Event Structure (Homogeneous)

Most event files (listens, likes, dislikes, undislikes, unlikes) share this base structure:

Sorting: All files are sorted by (uid, timestamp) in ascending order.

Unified Event Structure (Heterogeneous)

For applications needing all event types in a unified format:

Notes:

• played_ratio_pct and track_length_seconds are non-null only when event_type = "listen"
• All fields except the two above are guaranteed non-null

Sequential (Aggregated) Format

Each dataset is also available in a user-aggregated sequential format with the following structure:

Notes:

• All lists maintain chronological order
• For each user, len(item_ids) == len(timestamps) == len(is_organic)
• In multi-event format, null values are preserved in respective lists

Benchmark

Code for the baseline models can be found in benchmarks/ directory, see Reproducibility Guide

Download

Simplest way:

from datasets import load_dataset

ds = load_dataset("yandex/yambda", data_dir="flat/50m", data_files="likes.parquet")

Also, we provide simple wrapper for convenient usage:

from typing import Literal
from datasets import Dataset, DatasetDict, load_dataset

class YambdaDataset:
    INTERACTIONS = frozenset([
        "likes", "listens", "multi_event", "dislikes", "unlikes", "undislikes"
    ])

    def __init__(
        self,
        dataset_type: Literal["flat", "sequential"] = "flat",
        dataset_size: Literal["50m", "500m", "5b"] = "50m"
    ):
        assert dataset_type in {"flat", "sequential"}
        assert dataset_size in {"50m", "500m", "5b"}
        self.dataset_type = dataset_type
        self.dataset_size = dataset_size

    def interaction(self, event_type: Literal[
        "likes", "listens", "multi_event", "dislikes", "unlikes", "undislikes"
    ]) -> Dataset:
        assert event_type in YambdaDataset.INTERACTIONS
        return self._download(f"{self.dataset_type}/{self.dataset_size}", event_type)

    def audio_embeddings(self) -> Dataset:
        return self._download("", "embeddings")

    def album_item_mapping(self) -> Dataset:
        return self._download("", "album_item_mapping")

    def artist_item_mapping(self) -> Dataset:
        return self._download("", "artist_item_mapping")

    @staticmethod
    def _download(data_dir: str, file: str) -> Dataset:
        data = load_dataset("yandex/yambda", data_dir=data_dir, data_files=f"{file}.parquet")
        # Returns DatasetDict; extracting the only split
        assert isinstance(data, DatasetDict)
        return data["train"]

dataset = YambdaDataset("flat", "50m")
likes = dataset.interaction("likes")  # returns a HuggingFace Dataset

FAQ

Are test items presented in training data?

Not all, some test items do appear in the training set, others do not.

Are test users presented in training data?

Yes, there are no cold users in the test set.

How are audio embeddings generated?

Using a convolutional neural network inspired by Contrastive Learning of Musical Representations (J. Spijkervet et al., 2021).

What's the is_organic flag?

Indicates whether interactions occurred through organic discovery (True) or recommendation-driven pathways (False)

Which events are considered recommendation-driven?

Recommendation events include actions from:

• Personalized music feed
• Personalized playlists

What counts as a "listened" track or \(Listen_+\)?

A track is considered "listened" if over 50% of its duration is played.

What does it mean when played_ratio_pct is greater than 100?

A played_ratio_pct greater than 100% indicates that the user rewound and replayed sections of the track—so the total time listened exceeds the original track length. These values are expected behavior and not log noise. See discussion