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KDD
2004
ACM
170views Data Mining» more  KDD 2004»
14 years 10 months ago
Why collective inference improves relational classification
Procedures for collective inference make simultaneous statistical judgments about the same variables for a set of related data instances. For example, collective inference could b...
David Jensen, Jennifer Neville, Brian Gallagher
KDD
2009
ACM
298views Data Mining» more  KDD 2009»
14 years 4 months ago
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender system settings. Typical characteristics include: Only positive examples can be observed, ...
Rong Pan, Martin Scholz
ICDM
2007
IEEE
289views Data Mining» more  ICDM 2007»
14 years 4 months ago
Latent Dirichlet Conditional Naive-Bayes Models
In spite of the popularity of probabilistic mixture models for latent structure discovery from data, mixture models do not have a natural mechanism for handling sparsity, where ea...
Arindam Banerjee, Hanhuai Shan
AUSDM
2007
Springer
104views Data Mining» more  AUSDM 2007»
14 years 1 months ago
Effectiveness of Using Quantified Intermarket Influence for Predicting Trading Signals of Stock Markets
This paper investigates the use of influence from foreign stock markets (intermarket influence) to predict the trading signals, buy, hold and sell, of the of a given stock market....
Chandima Tilakaratne, Musa A. Mammadov, Sidney A. ...
ICDM
2009
IEEE
188views Data Mining» more  ICDM 2009»
13 years 7 months ago
Binomial Matrix Factorization for Discrete Collaborative Filtering
Matrix factorization (MF) models have proved efficient and well scalable for collaborative filtering (CF) problems. Many researchers also present the probabilistic interpretation o...
Jinlong Wu