Collecting ground truth, data augmentation, cascading heuristics and models, and more.
Industry examples, exploration strategies, warm-starting, off-policy evaluation, and more.
Focusing on long-term rewards, exploration, and frequently updated item.
How to generate labels from scratch with semi, active, and weakly supervised learning.
Breaking it into offline vs. online environments, and candidate retrieval vs. ranking steps.
A whirlwind tour of bandits, embedding+MLP, sequences, graph, and user embeddings.
An overview and comparison of the various approaches, with examples from industry search systems.
Access, serving, integrity, convenience, autopilot; use what you need.
Why real-time? How have China & US companies built them? How to design & build an MVP?
What questions do they answer? How do they compare? What open-source solutions are available?
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