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Modern Recommender Systems: from Computing Matrices to Thinking with Neurons

Summary: Tutorial on modern recommender systems spanning matrix factorization, bandits, and deep nets; large-scale examples illustrate capabilities. Covers evaluation challenges, future directions, and integrating recommender methods with database research. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5588
Venue
SIGMOD
Year
2018
Pagerank
4.5435639e-05
Overall Rank
8,296 | 42.29%
DOI
10.1145/3183713.3197389

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
11,591 GIANT: Scalable Creation of a Web-scale Ontology 2020 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank Cited Paper Year Venue Pagerank
460 SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics 2015 VLDB 0.00022516069
5,785 REACT: Context-Sensitive Recommendations for Data Analysis 2016 SIGMOD 5.3284468e-05
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