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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- tabular-classification
+- tabular-regression
+- graph-ml
+# - recommendation
+# - retrieval
+# - ranking
+# - user-modeling
+- other
+tags:
+- recommendation
+- recsys
+- short-video
+- clips
+- retrieval
+- ranking
+- user-modeling
+- industrial
+- real-world
+size_categories:
+- 10B<n<100B
+language:
+  - en
+pretty_name: VK-LSVD
+---
+
+
+# VK-LSVD: Large Short-Video Dataset
+**VK-LSVD** is the largest open industrial short-video recommendation dataset with real-world interactions: 
+- **40B** unique user–item interactions with rich feedback (`timespent`, `like`, `dislike`, `share`, `bookmark`, `click_on_author`, `open_comments`) and
+context (`place`, `platform`, `agent`);
+- **10M** users (with `age`, `gender`, `geo`);
+- **20M** short videos (with `duration`, `author_id`, content `embedding`);
+- **Global Temporal Ordering** across **six consecutive months** of user interactions.
+
+**Why short video?** Users often watch dozens of clips per session, producing dense, time-ordered signals well suited for modeling. 
+Unlike music, podcasts, or long-form video, which are often consumed in the background, short videos are foreground by design. They also do not exhibit repeat exposure.
+Even without explicit feedback, signals such as skips, completions, and replays yield strong implicit labels. 
+Single-item feeds also simplify attribution and reduce confounding compared with multi-item layouts. 
+
+
+---
+
+> **Note:** The test set will be released after the upcoming challenge.
+  
+---
+
+[📊 Basic Statistics](#basic-statistics) • [🧱 Data Description](#data-description) • [⚡ Quick Start](#quick-start) • [🧩 Configurable Subsets](#configurable-subsets)
+
+---
+
+## Basic Statistics
+- Users **10,000,000**
+- Items **19,627,601**
+- Unique interactions **40,774,024,903**
+- Interactions density **0.0208%**
+- Total watch time: **858,160,100,084 s**
+- Likes: **1,171,423,458**
+- Dislikes: **11,860,138**
+- Shares: **262,734,328**
+- Clicks on author: **84,632,666**
+- Comment opens: **481,251,593**
+
+---
+
+## Data Description
+**Privacy-preserving taxonomy** — all categorical metadata (`user_id`, `geo`, `item_id`, `author_id`, `place`, `platform`, `agent`) is anonymized into stable integer IDs (consistent across splits; no reverse mapping provided).
+
+### Interactions 
+[interactions](https://huggingface.co/datasets/deepvk/VK-LSVD/tree/main/interactions)   
+Each row is one observation (a short video shown to a user) with feedback and context. There are no repeated exposures of the same user–item pair.  
+**Global Temporal Split (GTS):** `train` / `validation` / `test` preserve time order — train on the past, validate/test on the future.  
+**Chronology:** Files are organized by weeks (e.g., week_XX.parquet); rows within each file are in increasing timestamp order. 
+
+> The test set will be published after the challenge.
+
+---
+| Field | Type | Description |
+|-----|----|-----------|
+|`user_id`|uint32|User identifier|
+|`item_id`|uint32|Video identifier|
+|`place`|uint8|Place: feed/search/group/… (24 ids)|
+|`platform`|uint8|Platform: Android/Web/TV/… (11 ids) |
+|`agent`|uint8|Agent/client: browser/app (29 ids)|
+|`timespent`|uint8|Watch time (0–255 seconds)|
+|`like`|boolean|User liked the video|
+|`dislike`|boolean|User disliked the video|
+|`share`|boolean|User shared the video|
+|`bookmark`|boolean|User bookmarked the video|
+|`click_on_author`|boolean|User opened author page|
+|`open_comments`|boolean|User opened the comments section |
+
+### Users metadata 
+[users_metadata.parquet](metadata/users_metadata.parquet)
+| Field | Type | Description |
+|-----|----|-----------|
+|`user_id`|uint32|User identifier|
+|`age`|uint8|Age (18-70 years)|
+|`gender`|uint8|Gender|
+|`geo`|uint8|Most frequent user location (80 ids)|
+|`train_interactions_rank`|uint32|Popularity rank for sampling (lower = more interactions)|
+
+### Items metadata 
+[items_metadata.parquet](metadata/items_metadata.parquet)
+
+| Field | Type | Description |
+|-----|----|-----------|
+|`item_id`|uint32|Video identifier|
+|`author_id`|uint32|Author identifier|
+|`duration`|uint8|Video duration (seconds)|
+|`train_interactions_rank`|uint32|Popularity rank for sampling (lower = more interactions)|
+
+### Embeddings: variable width
+**Embeddings are trained strictly on content** (video/description/audio, etc.) — no collaborative signal mixed in.  
+**Components are ordered**: the _dot product_ of the first n components approximates the _cosine_ similarity of the original production embeddings.  
+This lets researchers pick any dimensionality (**1…64**) to trade quality for speed and memory.
+
+  
+[item_embeddings.npz](metadata/item_embeddings.npz)   
+
+| Field | Type | Description |
+|-----|----|-----------|
+|`item_id`|uint32|Video identifier|
+|`embedding`|float16[64]|Item content embedding with ordered components|
+
+## Quick Start 
+
+
+### Load a small subsample
+
+
+```python
+from huggingface_hub import hf_hub_download
+import polars as pl
+import numpy as np
+
+subsample_name = 'up0.001_ip0.001'
+content_embedding_size = 32
+
+train_interactions_files = [f'subsamples/{subsample_name}/train/week_{i:02}.parquet'
+                            for i in range(25)]
+val_interactions_file = [f'subsamples/{subsample_name}/validation/week_25.parquet']
+
+metadata_files = ['metadata/users_metadata.parquet',
+                  'metadata/items_metadata.parquet',
+                  'metadata/item_embeddings.npz']
+
+for file in (train_interactions_files +
+             val_interactions_file +
+             metadata_files):
+    hf_hub_download(
+        repo_id='deepvk/VK-LSVD', repo_type='dataset',
+        filename=file, local_dir='VK-LSVD'
+    )
+
+train_interactions = pl.concat([pl.scan_parquet(f'VK-LSVD/{file}')
+                                for file in train_interactions_files])
+train_interactions = train_interactions.collect(engine='streaming')
+
+val_interactions = pl.read_parquet(f'VK-LSVD/{val_interactions_file[0]}')
+
+train_users = train_interactions.select('user_id').unique()
+train_items = train_interactions.select('item_id').unique()
+
+item_ids = np.load('VK-LSVD/metadata/item_embeddings.npz')['item_id']
+item_embeddings = np.load('VK-LSVD/metadata/item_embeddings.npz')['embedding']
+
+mask = np.isin(item_ids, train_items.to_numpy())
+item_ids = item_ids[mask]
+item_embeddings = item_embeddings[mask]
+item_embeddings = item_embeddings[:, :content_embedding_size]
+
+users_metadata = pl.read_parquet('VK-LSVD/metadata/users_metadata.parquet')
+items_metadata = pl.read_parquet('VK-LSVD/metadata/items_metadata.parquet')
+
+users_metadata = users_metadata.join(train_users, on='user_id')
+items_metadata = items_metadata.join(train_items, on='item_id')
+items_metadata = items_metadata.join(pl.DataFrame({'item_id': item_ids, 
+                                                   'embedding': item_embeddings}), 
+                                     on='item_id')
+```
+
+
+---
+
+
+## Configurable Subsets
+
+We provide several ready-made slices and simple utilities to compose your own subset that matches your task, data budget, and hardware. 
+You can control density via popularity quantiles (`train_interactions_rank`), draw random users,
+or pick specific time windows — while preserving the Global Temporal Split.
+
+Representative subsamples are provided for quick experiments:
+
+| Subset | Users | Items | Interactions | Density |
+|-----|----:|-----------:|-----------:|-----------:|
+|`whole`|10,000,000|19,627,601|40,774,024,903|0.0208%|
+|`ur0.1`|1,000,000|18,701,510|4,066,457,259|0.0217%|
+|`ur0.01`|100,000|12,467,302|407,854,360|0.0327%|
+|`ur0.01_ir0.01`|90,178|125,018|4,044,900|0.0359%|
+|`ur0.01_ip0.01`|99,893|196,277|191,625,941|0.9774%|
+|`up0.01_ip0.01`|100,000|196,277|1,417,906,344|7.2240%|
+|`up0.001_ip0.001`|10,000|19,628|47,976,280|24.4428%|
+|`up-0.9_ip-0.9`|8,939,432|17,654,817|2,861,937,212|0.0018%|
+
+
+- `urX` — X fraction of **r**andom **u**sers (e.g., `ur0.01` = 1% of users).
+- `ipX` — X fraction of **p**opular **i**tems (by `train_interactions_rank`)  
+- Negative X denotes the least-popular fraction (e.g., `−0.9` → bottom 90%).
+
+
+For example, to get [ur0.01_ip0.01](https://huggingface.co/datasets/deepvk/VK-LSVD/tree/main/subsamples/ur0.01_ip0.01) (1% of **r**andom **u**sers, 1% of most **p**opular **i**tems) use the snippet below.
+```python
+import polars as pl
+
+def get_sample(entries: pl.DataFrame, split_column: str, fraction: float) -> pl.DataFrame:
+    if fraction >= 0:
+        entries = entries.filter(pl.col(split_column) <= 
+                                 pl.col(split_column).quantile(fraction, 
+                                                               interpolation='midpoint'))
+    else:
+        entries = entries.filter(pl.col(split_column) >= 
+                                 pl.col(split_column).quantile(1 + fraction, 
+                                                               interpolation='midpoint'))
+    return entries
+
+users = pl.scan_parquet('VK-LSVD/metadata/users_metadata.parquet')
+users_sample = get_sample(users, 'user_id', 0.01).select(['user_id'])
+
+items = pl.scan_parquet('VK-LSVD/metadata/items_metadata.parquet')
+items_sample = get_sample(items, 'train_interactions_rank', 0.01).select(['item_id'])
+
+interactions = pl.scan_parquet('VK-LSVD/interactions/validation/week_25.parquet')
+interactions = interactions.join(users_sample, on='user_id', maintain_order='left')
+interactions = interactions.join(items_sample, on='item_id', maintain_order='left')
+
+interactions_sample = interactions.collect(engine='streaming')
+```
+
+To get [up-0.9_ip-0.9](https://huggingface.co/datasets/deepvk/VK-LSVD/tree/main/subsamples/up-0.9_ip-0.9) (90% of least **p**opular **u**sers, 90% of least **p**opular **i**tems) replace users and items sampling lines with 
+```python
+users_sample = get_sample(users, 'train_interactions_rank', -0.9).select(['user_id'])
+items_sample = get_sample(items, 'train_interactions_rank', -0.9).select(['item_id'])
+```