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@book{JuKa90,
author = {Karlgren, Jussi},
institution = {KTH, Computer and Systems Sciences, DSV},
institution = {Stockholm University},
publisher = {Department of Computer and Systems Sciences, Stockholm University},
title = {An algebra for recommendations : Using reader data as a basis for measuring document proximity},
series = {SYSLAB technical reports},
number = {179},
pages = {1--11},
abstract = {A measure for proximity between documents is defined, based on data from readers. This proximity measure can be further investigated as a tool document retrieval, and as to provide data for concept formation experiments. },
year = {1990}
}
@article{Rendle19,
author = {Steffen Rendle and
Li Zhang and
Yehuda Koren},
title = {On the Difficulty of Evaluating Baselines: {A} Study on Recommender
Systems},
journal = {CoRR},
volume = {abs/1905.01395},
year = {2019},
url = {http://arxiv.org/abs/1905.01395},
archivePrefix = {arXiv},
eprint = {1905.01395},
timestamp = {Mon, 27 May 2019 13:15:00 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1905-01395},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DeKa11,
author = {Christian Desrosiers and
George Karypis},
year = {2011},
month = {01},
pages = {107--144},
title = {A Comprehensive Survey of Neighborhood-Based Recommendation Methods},
booktitle = {Recommender Systems Handbook},
editor = {P.B. Kantor and F. Ricci and L. Rokach and B. Shapira},
publisher={Springer},
doi = {10.1007/978-0-387-85820-3_4}
}
@inproceedings{Lops11,
author = {Pasquale Lops and
Marco de Gemmis and
Giovanni Semeraro},
year = {2011},
month = {01},
pages = {74--105},
title = {Content-based Recommender Systems: State of the Art and Trends},
booktitle = {Recommender Systems Handbook},
editor = {P.B. Kantor and F. Ricci and L. Rokach and B. Shapira},
publisher={Springer},
doi = {10.1007/978-0-387-85820-3_4}
}
@inproceedings{Ferrari19,
author = {Maurizio Ferrari Dacrema and Paolo Cremonesi and Dietmar Jannach},
year = {2019},
month = {07},
pages = {},
title = {Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches},
isbn = {978-1-4503-6243-6},
doi = {10.1145/3298689.3347058}
}
@article{Kor09,
author = {Yehuda Koren and
Robert Bell and
Chris Volinsky},
year = {2009},
month = {08},
pages = {30-37},
title = {Matrix factorization techniques for recommender systems},
volume = {42},
journal = {Computer}
}
@inproceedings{Kor08,
author = {Koren, Yehuda},
year = {2008},
month = {08},
pages = {426-434},
title = {Factorization meets the neighborhood: A multifaceted collaborative filtering model},
journal = {Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'08).},
doi = {10.1145/1401890.1401944}
}
@inproceedings{Kor11,
author = {Yehuda Koren and Robert Bell},
year = {2011},
month = {01},
pages = {145--186},
title = {Advances in Collaborative Filtering},
booktitle = {Recommender Systems Handbook},
editor = {P.B. Kantor and F. Ricci and L. Rokach and B. Shapira},
publisher={Springer},
doi = {10.1007/978-0-387-85820-3_4}
}
@misc{Funk06,
author = {Simon Funk},
title = {Netflix Update: Try This at Home},
howpublished = {\url{https://sifter.org/~simon/journal/20061211.html}},
note = {Accessed: 2019-12-12},
year = {2006},
month = {12}
}
@inproceedings{Zh08,
author = {Yunhong Zhou and Dennis Wilkinson and Robert Schreiber and Rong Pan},
year = {2008},
month = {06},
pages = {337-348},
title = {Large-Scale Parallel Collaborative Filtering for the Netflix Prize},
doi = {10.1007/978-3-540-68880-8_32}
}
@inproceedings{Rus08,
author = {Ruslan Salakhutdinov and Andriy Mnih},
year = {2008},
month = {01},
pages = {880-887},
title = {Bayesian probabilistic matrix factorization using Markov chain Monte Carlo},
volume = {25},
journal = {Proceedings of the 25th International Conference on Machine Learning},
doi = {10.1145/1390156.1390267}
}
@misc{Koren,
author = {Yehuda Koren},
title = {Biography of Yehuda Koren},
howpublished = {\url{https://ieeexplore.ieee.org/author/37414256700}},
note = {Accessed: 2019-12-21},
}
@article{Kurucz07,
author = {Miklós Kurucz and András Benczúr and Károly Csalogány},
year = {2007},
month = {01},
pages = {},
title = {Methods for large scale SVD with missing values},
journal = {ACM KDDCup 2007}
}
@article{Koren09,
author = {Yehuda Koren},
year = {2009},
month = {09},
pages = {},
title = {The BellKor solution to the Netflix Grand Prize}
}
@article{Harper15,
author = {Harper, F. Maxwell and Konstan, Joseph A.},
title = {The MovieLens Datasets: History and Context},
journal = {ACM Trans. Interact. Intell. Syst.},
issue_date = {January 2016},
volume = {5},
number = {4},
month = dec,
year = {2015},
issn = {2160-6455},
pages = {19:1--19:19},
articleno = {19},
numpages = {19},
url = {http://doi.acm.org/10.1145/2827872},
doi = {10.1145/2827872},
acmid = {2827872},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {Datasets, MovieLens, ratings, recommendations},
}
@inproceedings{Rendle13,
author = {Steffen Rendle},
year = {2013},
month = {03},
pages = {337-348},
title = {Scaling factorization machines to relational data},
volume = {6},
journal = {Proceedings of the VLDB Endowment},
doi = {10.14778/2535573.2488340}
}
@article{Paterek07,
author = {Arkadiusz Paterek},
year = {2007},
month = {01},
pages = {},
title = {Improving regularized singular value decomposition for collaborative filtering},
journal = {Proceedings of KDD Cup and Workshop}
}