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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
11425
Venue
VLDB
Year
2016
Pagerank
6.8024576e-05
Overall Rank
4,254 | 70.82%
DOI
10.14778/3007263.3007267

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{sharma_vldb16,
        title = {{GraphJet: Real-Time Content Recommendations at Twitter}},
        author = {Sharma, Aneesh and Jiang, Jerry and Bommannavar, Praveen and Larson, Brian and Lin, Jimmy},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {13},
        pages = {1281--1284},
        doi = {10.14778/3007263.3007267},
        url = {https://doi.org/10.14778/3007263.3007267},
        year = {2016}
}

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