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methodology-11

Matrix Completion

3260 papers • 126 benchmarks • 313 datasets

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data. Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

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hiroyuki-kasai/OLSTEC

2 papers 31

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Netflix Prize

CAL500

SweetRS

SAVOIAS

Subtasks

Low-Rank Matrix Completion

Most implemented papers

Matrix completion and low-rank SVD via fast alternating least squares

T. Hastie, R. Mazumder, J. Lee, R. Zadeh•Wed Oct 08 2014

This article develops a software package softlmpute in R for implementing the two approaches for large matrix factorization and completion, and develops a distributed version for very large matrices using the Spark cluster programming environment.

567

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Introduction Benchmarks Datasets Subtasks Libraries Papers

jasonsun0310/MatrixCompletion.jl

2 papers 13

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Hybrid Recommender System based on Autoencoders

Florian Strub, Jérémie Mary, R. Gaudel•Thu Jun 23 2016

This paper enhanced the architecture of Recommender Systems by using a loss function adapted to input data with missing values, and by incorporating side information, demonstrating that while side information only slightly improve the test error averaged on all users/items, it has more impact on cold users/ items.

225 0

An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls

V. Chernozhukov, Kaspar Wüthrich, Yinchu Zhu•Sun Dec 24 2017

The causal inference problem is recast as a counterfactual prediction and a structural breaks testing problem to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably robust against misspecification.

234 0

Sequence-Aware Recommender Systems

P. Cremonesi, D. Jannach, Massimo Quadrana•Thu Feb 22 2018

A categorization of the corresponding recommendation tasks and goals is proposed, existing algorithmic solutions are summarized, methodological approaches when benchmarking what the authors call sequence-aware recommender systems are discussed, and open challenges in the area are outlined.

539 0

Unsupervised Metric Learning in Presence of Missing Data

A. Gilbert, Rishi Sonthalia•Wed Jul 18 2018

A new algorithm MR-MISSING is presented that extends these previous algorithms and can be used to compute low dimensional representation on data sets with missing entries and is provided with a theoretical guarantee under some simplifying assumptions.

15 0

Inductive Matrix Completion Based on Graph Neural Networks

Yixin Chen, Muhan Zhang•Thu Apr 25 2019

It is possible to train inductive matrix completion models without using side information while achieving similar or better performances than state-of-the-art transductive methods; local graph patterns around a (user, item) pair are effective predictors of the rating this user gives to the item; and long-range dependencies might not be necessary for modeling recommender systems.

267 0

GLocal-K: Global and Local Kernels for Recommender Systems

Taejun Lim, S. Han, Josiah Poon, Siqu Long, Bernd Burgstaller•Thu Aug 26 2021

This work proposes a G lobal-Local Kernel-based matrix completion framework, named GLocal-K, that aims to generalise and represent a high-dimensional sparse user-item matrix entry into a low dimensional space with a small number of important features.

50 0

Matrix Completion on Graphs

M. Bronstein, P. Vandergheynst, X. Bresson, Vassilis Kalofolias•Wed Aug 06 2014

This work introduces a novel matrix completion model that makes use of proximity information about rows and columns by assuming they form communities, and borrows ideas from manifold learning to constrain the solution to be smooth on these graphs, in order to implicitly force row and column proximities.

180 0

Collaborative Filtering with Graph Information: Consistency and Scalable Methods

Hsiang-Fu Yu, I. Dhillon, Nikhil S. Rao, Pradeep Ravikumar•Sun Dec 06 2015

This work formulate and derive a highly efficient, conjugate gradient based alternating minimization scheme that solves optimizations with over 55 million observations up to 2 orders of magnitude faster than state-of-the-art (stochastic) gradient-descent based methods.

294 0

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Matrix Completion | State-of-the-Art