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Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale

Summary: Serenade uses VMIS-kNN with a prebuilt index for session-based next-item prediction at scale, overcoming exponential session space. Production bol.com demonstrates ~1000 req/s and sub-7 ms 90th percentile latency across millions of items and 45M sessions. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6415
Venue
SIGMOD
Year
2022
Pagerank
5.2550158e-05
Overall Rank
9,527 | 34.64%
DOI
10.1145/3514221.3517901

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kersbergen_sigmod22,
        title = {{Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale}},
        author = {Kersbergen, Barrie and Sprangers, Olivier and Schelter, Sebastian},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517901},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517901},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
103 DuckDB: an Embeddable Analytical Database 2019 SIGMOD 0.00034161428
455 Differential dataflow 2013 CIDR 0.00018133241
2,642 CoHadoop: Flexible Data Placement and Its Exploitation in Hadoop 2011 VLDB 8.3059948e-05
2,929 StreamRec: A Real-Time Recommender System 2011 SIGMOD 7.9525409e-05
4,529 TencentRec: Real-time Stream Recommendation in Practice 2015 SIGMOD 6.6446748e-05
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