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Itembased Collaborative Filtering Recommendation Algorithms
 PROC. 10TH INTERNATIONAL CONFERENCE ON THE WORLD WIDE WEB
, 2001
"... ..."
Empirical Analysis of Predictive Algorithm for Collaborative Filtering
 Proceedings of the 14 th Conference on Uncertainty in Artificial Intelligence
, 1998
"... 1 ..."
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
Eigentaste: A Constant Time Collaborative Filtering Algorithm
, 2000
"... Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit realvalued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clusterin ..."
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Cited by 368 (6 self)
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Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit realvalued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline
On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
 STATISTICS AND COMPUTING
, 2000
"... In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework is develop ..."
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Cited by 1032 (76 self)
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In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework
NewsWeeder: Learning to Filter Netnews
 in Proceedings of the 12th International Machine Learning Conference (ML95
, 1995
"... A significant problem in many information filtering systems is the dependence on the user for the creation and maintenance of a user profile, which describes the user's interests. NewsWeeder is a netnewsfiltering system that addresses this problem by letting the user rate his or her interest l ..."
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Cited by 555 (0 self)
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A significant problem in many information filtering systems is the dependence on the user for the creation and maintenance of a user profile, which describes the user's interests. NewsWeeder is a netnewsfiltering system that addresses this problem by letting the user rate his or her interest
Explaining Collaborative Filtering Recommendations
, 2000
"... $XWRPDWHG FROODERUDWLYH ILOWHULQJ #$&)# V\VWHPV SUHGLFW D SHUVRQV DIILQLW\ IRU LWHPV RU LQIRUPDWLRQ E\ FRQQHFWLQJ WKDW SHUVRQV UHFRUGHG LQWHUHVWV ZLWK WKH UHFRUGHG LQWHUHVWV RI D FRPPXQLW\ RI SHRSOH DQG VKDULQJ UDWLQJV EHWZHHQ OLNH# PLQGHG SHUVRQV# +RZHYHU# FXUUHQW UHFRPPHQGHU V\VWHPV DUH EODFN ..."
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Cited by 394 (16 self)
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
Wrappers for Feature Subset Selection
 AIJ SPECIAL ISSUE ON RELEVANCE
, 1997
"... In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a ..."
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Cited by 1522 (3 self)
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In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
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Cited by 707 (18 self)
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The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new
Factor Graphs and the SumProduct Algorithm
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
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Cited by 1787 (72 self)
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be derived as specific instances of the sumproduct algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform algorithms.
Results 1  10
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