Learning to Rank for Reddit Search: A Project Retro
summary
Reddit integrates Learning to Rank into its existing Solr stack using the Solr LTR plugin and trains XGBoost-style tree models on click+dwell labels, adding negatives by sampling other queries. Features include title/body match, votes, recency and community signals; reranking runs per shard after lexical retrieval. Deployment uses Envoy shadowing and a 10% shadow traffic rollout, uncovers massive Java GC spikes from filter-cache growth, fixes by disabling filter-query caching, and yields measurable A/B improvements after a long 1–2 year effort.
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chapters (12)
- 0:00Introduction
- 1:24Why learning to rank
- 7:21Training data foundations
- 10:14Bias in training labels
- 12:01Feature selection strategy
- 15:11Solar LTR setup
- 17:59Search infrastructure
- 23:42Shadow traffic testing
- 25:06Garbage collection issues
- 32:10Key infrastructure lessons
- 36:02AB test results and next steps
- 42:05Q&A
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