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: h27ad7cbdfc63f0fd
Venue: SIGMOD
Year: 2018
Pagerank: 5.2905577e-05
Overall Rank: 8,699 | 41.52%
DOI: 10.1145/3183713.3197389

Incoming Non-self Citations Over Time

Authors

1. Georgia Koutrika (Athena Research Center)

BibTeX Citation

@inproceedings{koutrika_sigmod18,
        title = {{Modern Recommender Systems: from Computing Matrices to Thinking with Neurons}},
        author = {Koutrika, Georgia},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3197389},
        url = {https://dl.acm.org/doi/10.1145/3183713.3197389},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
12,088	GIANT: Scalable Creation of a Web-scale Ontology	2020	SIGMOD	4.9793485e-05

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
395	SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics	2015	VLDB	0.00019165452
6,933	REACT: Context-Sensitive Recommendations for Data Analysis	2016	SIGMOD	5.6406682e-05

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