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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
6353
Venue
SIGMOD
Year
2022
Pagerank
4.3349007e-05
Overall Rank
9,466 | 34.15%
DOI
10.1145/3514221.3517901

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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
185 DuckDB: an Embeddable Analytical Database 2019 SIGMOD 0.00036538405
522 Differential dataflow 2013 CIDR 0.00021099241
1,790 StreamRec: A Real-Time Recommender System 2011 SIGMOD 0.00010551363
2,439 CoHadoop: Flexible Data Placement and Its Exploitation in Hadoop 2011 VLDB 8.8190594e-05
4,439 TencentRec: Real-time Stream Recommendation in Practice 2015 SIGMOD 6.1885354e-05
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