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Title: Add tweet ranking algorithm documentation and optimization guide by Copilot · Pull Request #1 · ScriptSynth/the-algorithm · GitHub

Open Graph Title: Add tweet ranking algorithm documentation and optimization guide by Copilot · Pull Request #1 · ScriptSynth/the-algorithm

X Title: Add tweet ranking algorithm documentation and optimization guide by Copilot · Pull Request #1 · ScriptSynth/the-algorithm

Description: Repository lacked user-facing documentation explaining how the ranking algorithm works and how to optimize content for it. Documentation Added docs/TWEET_RANKING_GUIDE.md (796 lines) - Complete technical breakdown of the ranking pipeline: 6-stage architecture: candidate generation → feature hydration → ML scoring → filtering → reranking → mixing ~6,000 features per candidate (author, content, engagement, graph, temporal) ML models: Heavy Ranker (Navi), Light Ranker, Phoenix scorer In-network vs out-of-network handling (OON gets 0.75x penalty) Practical optimization strategies for content creators Implementation guide for building similar systems with code examples docs/RANKING_QUICK_REFERENCE.md (299 lines) - Concise reference: Ranking signal impact table (likes/retweets/replies = high, "not interested" = very negative) Best practices and anti-patterns Minimal viable ranker implementation Key metrics to track README.md - Added "Understanding Tweet Ranking" section with links to guides Key Technical Details Documented Pipeline narrows ~1B tweets → ~100 shown via multi-stage filtering Candidate sources: Earlybird (in-network), UTEG (graph-based), TweetMixer (blended), FRS (follow recs) Feature systems: SimClusters, TwHIN embeddings, Real Graph, TweepCred Scoring uses multi-task neural networks predicting engagement probabilities Diversity filters enforce author balance, content variety, deduplication Example of simplified ranker from guide: def rank_content(user_id, candidates): scored = [] for item in candidates: recency = 1.0 / (1 + hours_since_post(item)) engagement = (item.likes + 2*item.retweets + 3*item.replies) / (1 + item.impressions) relevance = compute_relevance(user_id, item) score = 0.3*recency + 0.4*engagement + 0.3*relevance scored.append((item, score)) scored.sort(key=lambda x: x[1], reverse=True) return [item for item, score in scored[:100]] Targets two audiences: content creators optimizing for algorithmic reach, and engineers building recommendation systems. ✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.

Open Graph Description: Repository lacked user-facing documentation explaining how the ranking algorithm works and how to optimize content for it. Documentation Added docs/TWEET_RANKING_GUIDE.md (796 lines) - Complete t...

X Description: Repository lacked user-facing documentation explaining how the ranking algorithm works and how to optimize content for it. Documentation Added docs/TWEET_RANKING_GUIDE.md (796 lines) - Complete t...

Opengraph URL: https://github.com/ScriptSynth/the-algorithm/pull/1

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