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Results 1 - 10
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7,055
Learning Collaborative Information Filters
- In Proc. 15th International Conf. on Machine Learning
, 1998
"... Predicting items a user would like on the basis of other users’ ratings for these items has become a well-established strategy adopted by many recommendation services on the Internet. Although this can be seen as a classification problem, algo-rithms proposed thus far do not draw on results from the ..."
Abstract
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Cited by 354 (4 self)
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in order to become predictors for one another's preferences. We evaluate the proposed algorithm on a large database of user ratings for motion pictures and find that our approach significantly out-performs current collaborative filtering algorithms.
Item-based 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 ..."
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
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Cited by 727 (18 self)
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boosting algorithm for combining preferences called RankBoost. We also describe an efficient implementation of the algorithm for certain natural cases. We discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different WWW
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 netnews-filtering system that addresses this problem by letting the user rate his or her interest l ..."
Abstract
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Cited by 561 (0 self)
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) principle was able to raise the percentage of interesting articles to be shown to users from 14% to 52% on average. Further, this performance significantly outperformed (by 21%) one of the most successful techniques in Information Retrieval (IR), termfrequency /inverse-document-frequency (tf-idf) weighting
A survey of trust and reputation systems for online service provision
, 2005
"... Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to collect information about potential service providers in order to select the most reliable and trustworthy provider of services and information and to avoid th ..."
Abstract
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Cited by 632 (15 self)
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the less trustworthy. A natural side effect is that it also provides an incentive for good behaviour and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative
Maximizing the Spread of Influence Through a Social Network
- In KDD
, 2003
"... Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of ..."
Abstract
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Cited by 990 (7 self)
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for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks. We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform
Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories
, 2004
"... Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been te ..."
Abstract
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Cited by 784 (16 self)
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Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been
Sequential Monte Carlo Methods for Dynamic Systems
- Journal of the American Statistical Association
, 1998
"... A general framework for using Monte Carlo methods in dynamic systems is provided and its wide applications indicated. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ..."
Abstract
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Cited by 664 (13 self)
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algorithm by combining desirable features. In addition, we propose a general use of Rao-Blackwellization to improve performances. Examples from econometrics and engineering are presented to demonstrate the importance of Rao-Blackwellization and to compare different Monte Carlo procedures. Keywords: Blind
Latent dirichlet allocation
- Journal of Machine Learning Research
, 2003
"... We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, ..."
Abstract
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Cited by 4365 (92 self)
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for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model. 1.
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