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 • ❓ 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.

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

Dataset	Users	Items	Listens	Likes	Dislikes
Yambda-50M	10,000	934,057	46,467,212	881,456	107,776
Yambda-500M	100,000	3,004,578	466,512,103	9,033,960	1,128,113
Yambda-5B	1,000,000	9,390,623	4,649,567,411	89,334,605	11,579,143

User History Length Distribution

Item Interaction Count

Data Format

File Descriptions

File	Description	Schema
listens.parquet	User listening events with playback details	uid, item_id, timestamp, is_organic, played_ratio_pct, track_length_seconds
likes.parquet	User like actions	uid, item_id, timestamp, is_organic
dislikes.parquet	User dislike actions	uid, item_id, timestamp, is_organic
undislikes.parquet	User undislike actions (reverting dislikes)	uid, item_id, timestamp, is_organic
unlikes.parquet	User unlike actions (reverting likes)	uid, item_id, timestamp, is_organic
embeddings.parquet	Track audio-embeddings	item_id, embed, normalized_embed

Common Event Structure (Homogeneous)

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

Field	Type	Description
uid	uint32	Unique user identifier
item_id	uint32	Unique track identifier
timestamp	uint32	Delta times, binned into 5s units.
is_organic	uint8	Boolean flag (0/1) indicating if the interaction was algorithmic (0) or organic (1)

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:

Field	Type	Description
uid	uint32	Unique user identifier
item_id	uint32	Unique track identifier
timestamp	uint32	Timestamp binned into 5s units.granularity
is_organic	uint8	Boolean flag for organic interactions
event_type	enum	One of: listen, like, dislike, unlike, undislike
played_ratio_pct	Optional[uint16]	Percentage of track played (1-100), null for non-listen events
track_length_seconds	Optional[uint32]	Total track duration in seconds, null for non-listen events

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:

Field	Type	Description
uid	uint32	Unique user identifier
item_ids	List[uint32]	Chronological list of interacted track IDs
timestamps	List[uint32]	Corresponding interaction timestamps
is_organic	List[uint8]	Corresponding organic flags for each interaction
played_ratio_pct	List[Optional[uint16]]	(Only in listens and multi_event) Play percentages
track_length_seconds	List[Optional[uint32]]	(Only in listens and multi_event) Track durations

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

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 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 $Liste{n}_{+}Listen\_+$?

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