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STAIRS
2008
169views Education» more  STAIRS 2008»
13 years 9 months ago
Probabilistic Association Rules for Item-Based Recommender Systems
Since the beginning of the 1990's, the Internet has constantly grown, proposing more and more services and sources of information. The challenge is no longer to provide users ...
Sylvain Castagnos, Armelle Brun, Anne Boyer
AAAI
2010
13 years 9 months ago
Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures
Matrix factorization is a fundamental technique in machine learning that is applicable to collaborative filtering, information retrieval and many other areas. In collaborative fil...
Ian Porteous, Arthur Asuncion, Max Welling
ICML
2007
IEEE
14 years 8 months ago
Learning a meta-level prior for feature relevance from multiple related tasks
In many prediction tasks, selecting relevant features is essential for achieving good generalization performance. Most feature selection algorithms consider all features to be a p...
Su-In Lee, Vassil Chatalbashev, David Vickrey, Dap...
KDD
2004
ACM
150views Data Mining» more  KDD 2004»
14 years 8 months ago
Complete This Puzzle: A Connectionist Approach to Accurate Web Recommendations Based on a Committee of Predictors
Abstract. We present a Context Ultra-Sensitive Approach based on two-step Recommender systems (CUSA-2step-Rec). Our approach relies on a committee of profile-specific neural networ...
Olfa Nasraoui, Mrudula Pavuluri
ICML
2007
IEEE
14 years 8 months ago
Learning to rank: from pairwise approach to listwise approach
The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative fil...
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Han...