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ItemSuggest: A Data Management Platform for Machine Learned Ranking Services

Summary: ItemSuggest is a data-management–centric platform for building contextual ML ranking services that streamlines training-data collection, quality validation/monitoring, feature lifecycle, model training/eval and A/B testing to maximize experiment velocity. It reports large-scale production lessons and highlights research avenues in transformation engines for feature engineering and compact training-set representations. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
331
Venue
CIDR
Year
2019
Pagerank
4.7364436e-05
Overall Rank
7,411 | 48.45%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,438 Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation 2021 CIDR 4.3425082e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1,277 The Data Civilizer System 2017 CIDR 0.00012879695
1,420 Data Management Challenges in Production Machine Learning 2017 SIGMOD 0.00012057956
1,532 Data Management in Machine Learning: Challenges, Techniques, and Systems 2017 SIGMOD 0.00011472681
6,615 Building Machine Learning Systems that Understand 2016 SIGMOD 4.9936072e-05
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