--- 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 **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** - Bookmarks: **40,124,463** - 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. | 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%| |`up0.01_ir0.01`|100000|171106|38,404,921|0.2245%| |`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']) ```