The Netflix Prize

By J. Bennett, S. Lanning. 2007

2,341 citations recorded by the source corpus.

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Citations in the corpus

Quantization based Fast Inner Product Search (2015, background): “Given fixed a, > 0, let F(a, ) be an event such that the exact dot product qTx is at least a, but the quantized version is either smaller than qTx(1− ) or larger than qTx(1 + ).”
Maximum inner-product search using cone trees (2012, methodology): “Secondly, the dual-tree algorithm (especially dual ball-cone) starts outperforming the single-tree algorithm sig BioCorelCovertype LCDMLiveJournal MNISTMovieLens NetflixOptDigitsPall7 Physics PSFSJ2URandY!”
Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016, positive): “Collaborative Filtering (CF), especially Matrix Factorization approaches, have seen wide success at accurately modeling users’ preferences, perhaps most notably for the Netflix Prize [2, 4, 20].”
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015, background): “Such systems play a central role in helping people discover items of personal interest from huge corpora, ranging from movies and music (Bennett and Lanning 2007; Koenigstein, Dror, and Koren 2011), to research articles, news and books (Das et al.”
From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, background): “Sophisticated models of such temporal dynamics proved critical in obtaining state-of-the-art performance on the Netflix challenge [3], most famously in [20].”
A Neural Autoregressive Approach to Collaborative Filtering (2016, methodology): “For example, for the Netﬂix dataset (Bennett & Lanning, 2007), when H = 500 , the number of free parameters by W k and V k would be around 89 million 2 .”
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, methodology): “Rating prediction is a common focus for recommendation algorithms, owing its popularity at least in part to the Netflix challenge [4, 5], which evaluated systems using RMSE on a set of held-out data.”
Manipulating and Measuring Model Interpretability (2018, background): “, Netflix, [7]) or what music to listen to on online streaming platforms (e.”
Machine Learning that Matters (2012, background): “They diﬀer from existing challenges such as the DARPA Grand Challenge (Buehler et al., 2007), the Netﬂix Prize (Bennett & Lanning, 2007), and the Yahoo!”
XGBoost: A Scalable Tree Boosting System (2016, methodology): “Finally, it is the defacto choice of ensemble method and is used in challenges such as the Netflix prize [3].”
Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time (2017, background): “However, classical recommender systems that consider catalogs that only contain millions of items (movies [5, 18], videos [4, 7, 10], friends to follow [2, 14, 15]).”
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction (2019, background): “In some domains, such as autonomous driving [39] and recommender systems [4], previously collected datasets are plentiful.”
Feature hashing for large scale multitask learning (2009, neutral): “We ﬁrst deﬁne a weighted Hamming distance function between two hash-function φ and φ ′ as follows. p Next denote by d ( φ, A ) the distance between a hash function and a set A of hash functions, that is d ( φ, A ) = inf φ ′ ∈ A d ( φ, φ ′ ) .”
Ordinal Common-sense Inference (2016, background): “uations in ordinal prediction tasks (Baccianella et al., 2009; Bennett and Lanning, 2007; Gaudette and Japkowicz, 2009; Agresti, 2003; Popescu and Dinu, 2009; Liu et al., 2015; Gella et al., 2013).”
Learning Attitudes and Attributes from Multi-aspect Reviews (2012, background): “Reviews consisting of plain-text feedback and a single numeric score have proved a valuable source of data in many applications, including product recommendation [2], feature discovery [27], review summarization [16], and sentiment analysis [33], among others.”
Accurate estimation of influenza epidemics using Google search data via ARGO (2015, background): “Numerous studies have proposed early prototypes, showing great potential of these big data sets to detect and manage epidemic outbreaks (Influenza [1, 2, 3, 4, 5, 6], Ebola [7], Dengue [8]), predict changes in stock prices [9, 10] and housing prices [11], estimate users’ movie preferences [12], etc.”
HOList: An Environment for Machine Learning of Higher-Order Theorem Proving (extended version) (2019, background): “Well-defined, large-scale benchmarks were instrumental for unifying disparate efforts in machine learning research: LibriSpeech [1] for speech recognition, the Netflix prize [2] for recommendation, ImageNet [3] for object recognition, MSCOCO [4] for object detection and segmentation, WMT [5] for mac”