KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
Source
- Raw Markdown: paper_kuairand-2022.md
- PDF: paper_kuairand-2022.pdf
Core Claim
KuaiRand provides sequential recommendation logs with randomly exposed videos, making action exposure less confounded than ordinary recommender logs.
Action-Time-Series Notes
- The time-series unit is a user interaction sequence over recommended/exposed videos and feedback signals.
- The action is the item/video exposure; feedback signals are response observations or rewards.
- It is much more suitable for action-response user models than one-step ad-click logs.
Foundation TSFM Relevance
| Agenda slot | Verdict | Evidence | Missing pieces |
|---|---|---|---|
| Causal structure, counterfactuals, and control | partially closes | Randomly exposed videos give large-scale intervened interaction logs with item exposures as actions and 12 user feedback signals as outcomes. | Recommendation actions are randomized exposures, not system-control interventions with long-horizon counterfactual rollout. |
| Time representation and irregular event streams | adjacent | Provides timestamped sequential user interactions and behavior histories. | Needs explicit irregular-time modeling contracts and event-duration semantics. |
| Benchmarks: what level of modeling is tested? | adjacent | Supports unbiased offline evaluation, debiasing, interactive recommendation, long sequential behavior modeling, and multi-task learning. | Does not evaluate general latent-state TSFM forecasting or control utility. |