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GraphJet: Real-Time Content Recommendations at Twitter

Summary: In-memory, single-server graph engine for real-time user–tweet bipartite recommendations. Temporal-partitioned adjacency, compact edge encoding, and power-law aware memory allocation enable high-throughput ingestion (≈1M edges/sec) and ~500 recommendations/sec via random-walk-based algorithms. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11237
Venue
VLDB
Year
2016
Pagerank
5.8534354e-05
Overall Rank
4,885 | 66.02%
DOI
-

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