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This paper proposes Representation Curriculum (RC), a training-time intervention that stages feature utilization to reduce over-reliance on exposure-confounded historical signals and improve cold-start generalization in ranking systems. The method is theoretically analyzed and validated on public benchmarks and large-scale eBay search experiments.
A discussion on how AI agents should handle user context: upfront disclosure or gradual learning, with various existing approaches like project memory and chat summaries found lacking.
Introduces CausalPOI, a spatio-temporal graph-based causal representation learning framework for cold-start POI check-in forecasting, which outperforms state-of-the-art baselines on real-world SafeGraph datasets.
PaperFlow is a framework for scientific paper recommendation that processes user profiles, daily paper streams, and interest drift through three stages: profiling, recommending, and adapting, evaluated on a longitudinal benchmark with 24 users and 50 daily streams.
The paper introduces Human-in-the-Loop Gated Bandit (HITL-GB) for short-term rental dynamic pricing, showing that historical pricing data under a prior policy is structurally equivalent to on-policy warm-up data, reducing cold-start from ~150 to ~30 episodes.
Browser Use launches a new browser infrastructure service featuring subsecond cold starts, lower cost at $0.02/h, and unlimited scaling, now live for developers.
Discusses the cold start problem in AI personalization, where new products lack user data, and proposes a unified user data API as a potential solution that is consented and user-owned.
Modal explains how it reduces AI inference cold starts by 40x using cloud buffers, a custom filesystem, checkpoint/restore, and CUDA checkpoint/restore, framing cloud buffer management as a linear optimization problem solved with GLOP.
A software engineer asks for strategies to bootstrap personalization for new users with no behavioral data, discussing the cold-start problem in content recommendation.
TopoPrimer is a framework that improves forecasting accuracy by integrating global topological structures into existing models, showing significant gains in challenging scenarios like seasonal spikes and cold starts.