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New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy

by Zhengzheng Xian , Qiliang Li , Gai Li , Lei Li
"... Collaborative filtering technology has been widely used in the recommender system, and its implementation is supported by the large amount of real and reliable user data from the big-data era. However, with the increase of the users' information-security awareness, these data are reduced or th ..."
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Collaborative filtering technology has been widely used in the recommender system, and its implementation is supported by the large amount of real and reliable user data from the big-data era. However, with the increase of the users' information-security awareness, these data are reduced

Item-based Collaborative Filtering Recommendation Algorithms

by Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl - PROC. 10TH INTERNATIONAL CONFERENCE ON THE WORLD WIDE WEB , 2001
"... ..."
Abstract - Cited by 1198 (34 self) - Add to MetaCart
Abstract not found

Empirical Analysis of Predictive Algorithm for Collaborative Filtering

by John S. Breese, David Heckerman, Carl Kadie - Proceedings of the 14 th Conference on Uncertainty in Artificial Intelligence , 1998
"... 1 ..."
Abstract - Cited by 1497 (4 self) - Add to MetaCart
Abstract not found

An Efficient Boosting Algorithm for Combining Preferences

by Raj Dharmarajan Iyer , Jr. , 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 727 (18 self) - Add to MetaCart
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

Fab: Content-based, collaborative recommendation

by Marko Balabanovic, Yoav Shoham - Communications of the ACM , 1997
"... Fab is a recommendation system designed to help users sift through the enormous amount of information available in the World Wide Web. Operational since Dec. 1994, this system combines the content-based and collaborative methods of recommendation in a way that exploits the advantages of the two appr ..."
Abstract - Cited by 682 (0 self) - Add to MetaCart
for Computing Machinery Inc. By combining both collaborative and content-based filtering systems, Fab may eliminate many of the weaknesses found in each approach. Online readers are in need of tools to help them cope with the mass of content available on the World-Wide Web. In traditional media, readers

Articulated body motion capture by annealed particle filtering

by Jonathan Deutscher, Andrew Blake, Ian Reid - In IEEE Conf. on Computer Vision and Pattern Recognition , 2000
"... The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30) to be recovered. Search algorithms, either deterministic or stochastic, that search such a space without constraint, fall foul of exponential computational complexity. One approach is to intr ..."
Abstract - Cited by 494 (4 self) - Add to MetaCart
in the fitness function, gradually. The new algorithm, termed annealed particle filtering, is shown to be capable of recovering full articulated body motion efficiently. 1.

The ensemble Kalman Filter: Theoretical formulation and practical implementation.

by Geir Evensen - Ocean Dynamics, , 2003
"... Abstract The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group and numerous publications have discussed applications and theoretical aspects of it. This paper rev ..."
Abstract - Cited by 496 (5 self) - Add to MetaCart
reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal

The "Independent Components" of Natural Scenes are Edge Filters

by Anthony J. Bell, Terrence J. Sejnowski , 1997
"... It has previously been suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and it has been reasoned that such responses should emerge from an unsupervised learning algorithm that attem ..."
Abstract - Cited by 617 (29 self) - Add to MetaCart
that attempts to find a factorial code of independent visual features. We show here that a new unsupervised learning algorithm based on information maximization, a nonlinear "infomax" network, when applied to an ensemble of natural scenes produces sets of visual filters that are localized and oriented

Eigentaste: A Constant Time Collaborative Filtering Algorithm

by Ken Goldberg, Theresa Roeder, Dhruv Gupta, Chris Perkins , 2000
"... Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued 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 ..."
Abstract - Cited by 378 (6 self) - Add to MetaCart
Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued 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

Latent Semantic Models for Collaborative filtering

by Thomas Hofmann - ACM Trans. Information Systems
"... Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, that is, a database of available user preferences. In this article, we describe a new family of model-based algorithms designed for this task. These algorithms rely on a statist ..."
Abstract - Cited by 331 (1 self) - Add to MetaCart
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, that is, a database of available user preferences. In this article, we describe a new family of model-based algorithms designed for this task. These algorithms rely on a
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