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2,300
A Singular Value Thresholding Algorithm for Matrix Completion
, 2008
"... This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of reco ..."
Abstract
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Cited by 555 (22 self)
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of recovering a large matrix from a small subset of its entries (the famous Netflix problem). Off-the-shelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries. This paper develops a simple first-order and easy
An Introduction to the Kalman Filter
- UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL
, 1995
"... In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area o ..."
Abstract
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Cited by 1146 (13 self)
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In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area
The Power of Convex Relaxation: Near-Optimal Matrix Completion
, 2009
"... This paper is concerned with the problem of recovering an unknown matrix from a small fraction of its entries. This is known as the matrix completion problem, and comes up in a great number of applications, including the famous Netflix Prize and other similar questions in collaborative filtering. In ..."
Abstract
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Cited by 359 (7 self)
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This paper is concerned with the problem of recovering an unknown matrix from a small fraction of its entries. This is known as the matrix completion problem, and comes up in a great number of applications, including the famous Netflix Prize and other similar questions in collaborative filtering
Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms
- J. ACM
, 1999
"... In this paper, we establish max-flow min-cut theorems for several important classes of multicommodity flow problems. In particular, we show that for any n-node multicommodity flow problem with uniform demands, the max-flow for the problem is within an O(log n) factor of the upper bound implied by ..."
Abstract
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Cited by 357 (6 self)
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by the min-cut. The result (which is existentially optimal) establishes an important analogue of the famous 1-commodity max-flow min-cut theorem for problems with multiple commodities. The result also has substantial applications to the field of approximation algorithms. For example, we use the flow result
Differentially Private Recommender Systems: Building Privacy into the Netflix Prize Contenders
"... We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these users. Specifically, we consider the Netflix Prize data set, and its leading algorithms, adapted to the framework of differential privacy. Unlike prior pr ..."
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Cited by 128 (3 self)
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We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these users. Specifically, we consider the Netflix Prize data set, and its leading algorithms, adapted to the framework of differential privacy. Unlike prior
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
- Proc. 4th Int’l Conf. Algorithmic Aspects in Information and Management, LNCS 5034
, 2008
"... Abstract. Many recommendation systems suggest items to users by utilizing the techniques of collaborative filtering (CF) based on historical records of items that the users have viewed, purchased, or rated. Two major problems that most CF approaches have to resolve are scalability and sparseness of ..."
Abstract
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Cited by 96 (1 self)
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Abstract. Many recommendation systems suggest items to users by utilizing the techniques of collaborative filtering (CF) based on historical records of items that the users have viewed, purchased, or rated. Two major problems that most CF approaches have to resolve are scalability and sparseness
Collaborative filtering with temporal dynamics
- In Proc. of KDD ’09
, 2009
"... Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing reco ..."
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Cited by 246 (4 self)
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and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instancedecay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required
Quantum measurements and the Abelian stabilizer problem
"... We present a polynomial quantum algorithm for the Abelian stabilizer problem which includes both factoring and the discrete logarithm. Thus we extend famous Shor’s results [7]. Our method is based on a procedure for measuring an eigenvalue of a unitary operator. Another application of this procedure ..."
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Cited by 196 (0 self)
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We present a polynomial quantum algorithm for the Abelian stabilizer problem which includes both factoring and the discrete logarithm. Thus we extend famous Shor’s results [7]. Our method is based on a procedure for measuring an eigenvalue of a unitary operator. Another application
Use of KNN for the Netflix Prize
"... This paper analyzes the performance of various KNNs techniques as applied to the netflix collaborative filtering problem. 1. ..."
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Cited by 1 (0 self)
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This paper analyzes the performance of various KNNs techniques as applied to the netflix collaborative filtering problem. 1.
CS 277: The Netflix Prize
"... 100k movies, 10 million customers Ships 1.9 million disks to customers each day – 50 warehouses in the US – Complex logistics problem • Employees: 2000 – But relatively few in engineering/software – And only a few people working on recommender systems Moving towards online delivery of content Signif ..."
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100k movies, 10 million customers Ships 1.9 million disks to customers each day – 50 warehouses in the US – Complex logistics problem • Employees: 2000 – But relatively few in engineering/software – And only a few people working on recommender systems Moving towards online delivery of content
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