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202,578
Evaluating collaborative filtering recommender systems
 ACM TRANSACTIONS ON INFORMATION SYSTEMS
, 2004
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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 ..."
Abstract

Cited by 394 (16 self)
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
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 ..."
Abstract

Cited by 707 (18 self)
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search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborativefiltering task for making movie recommendations. Here, we present results comparing Rank
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
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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level for each article being read (15), and then learning a user profile based on these ratings. This paper describes how NewsWeeder accomplishes this task, and examines the alternative learning methods used. The results show that a learning algorithm based on the Minimum Description Length (MDL
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
Toward the next generation of recommender systems: A survey of the stateoftheart and possible extensions
 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2005
"... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: contentbased, collaborative, and hybrid recommendation approaches. This paper also describes vario ..."
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Cited by 1420 (21 self)
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This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: contentbased, collaborative, and hybrid recommendation approaches. This paper also describes
A learning algorithm for Boltzmann machines
 Cognitive Science
, 1985
"... The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a probl ..."
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Cited by 586 (13 self)
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problem in o very short time. One kind of computation for which massively porollel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found
Algorithms for Scalable Synchronization on SharedMemory Multiprocessors
 ACM Transactions on Computer Systems
, 1991
"... Busywait techniques are heavily used for mutual exclusion and barrier synchronization in sharedmemory parallel programs. Unfortunately, typical implementations of busywaiting tend to produce large amounts of memory and interconnect contention, introducing performance bottlenecks that become marke ..."
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Cited by 567 (32 self)
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markedly more pronounced as applications scale. We argue that this problem is not fundamental, and that one can in fact construct busywait synchronization algorithms that induce no memory or interconnect contention. The key to these algorithms is for every processor to spin on separate locally
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
, 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
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Cited by 2109 (30 self)
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. The core of this method is a simple hillclimbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distancebased method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment
Results 1  10
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202,578