Open AccessProceedings Article
Collaborative ensemble learning: combining collaborative and content-based information filtering via hierarchical bayes
Kai Yu,Anton Schwaighofer,Volker Tresp +2 more
- 07 Aug 2002
- pp 616-623
TL;DR: In this paper, the authors proposed a collaborative ensemble learning framework to unify collaborative filtering and content-based filtering, which combines a society of users' preferences to predict an active user's preferences.
read more
Abstract: Collaborative filtering (CF) and content-based filtering (CBF) have widely been used information filtering applications, both approaches having their individual strengths and weaknesses. This paper proposes a novel probabilistic framework to unify CF and CBF, named collaborative ensemble learning. Based on content based probabilistic models for each user's preferences (the CBF idea), it combines a society of users' preferences to predict an active user's preferences (the CF idea). While retaining an intuitive explanation, the combination scheme can be interpreted as a hierarchical Bayesian approach in which a common prior distribution is learned from related experiments. It does not require a global training stage and thus can incrementally incorporate new data. We report results based on two data sets, the neuters-21578 text data set and a data base of user opionions on art images. For both data sets, collaborative ensemble achieved excellent performance in terms of recommendation accuracy. In addition to recommendation engines, collaborative ensemble learning is applicable to problems typically solved via classical hierarchical Bayes, like multisensor fusion and multitask learning.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Journal Article
Multi-Task Learning for Classification with Dirichlet Process Priors
TL;DR: Experimental results on two real life MTL problems indicate that the proposed algorithms automatically identify subgroups of related tasks whose training data appear to be drawn from similar distributions are more accurate than simpler approaches such as single-task learning, pooling of data across all tasks, and simplified approximations to DP.
Multi-Task Compressive Sensing
Shihao Ji,David B. Dunson,Lawrence Carin +2 more
- 01 Jan 2007
TL;DR: This paper addresses the problem within a multi-task learning setting, wherein the mapping vi !
446
Probabilistic memory-based collaborative filtering
TL;DR: It is shown that a probabilistic active learning method can be used to actively query the user, thereby solving the "new user problem" of memory-based collaborative filtering.
•Proceedings Article
Learning Gaussian Process Kernels via Hierarchical Bayes
Anton Schwaighofer,Volker Tresp,Kai Yu +2 more
- 01 Dec 2004
TL;DR: This work presents a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework, and evaluates the approach as a recommendation engine for art images, where the proposed hierarchicalBayesian method leads to excellent prediction performance.
Integrating Concept Ontology and Multitask Learning to Achieve More Effective Classifier Training for Multilevel Image Annotation
TL;DR: A novel hyperbolic framework for large-scale image visualization and interactive hypotheses assessment and a novel hierarchical boosting algorithm is developed to learn their ensemble classifiers hierarchically is developed.
173
References
•Book
The Nature of Statistical Learning Theory
Vladimir Vapnik
- 01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
46K
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
Thorsten Joachims
- 21 Apr 1998
TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.
Multitask Learning
Rich Caruana
- 01 Jul 1997
TL;DR: Multi-task Learning (MTL) as mentioned in this paper is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias.
GroupLens: an open architecture for collaborative filtering of netnews
Paul Resnick,Neophytos Iacovou,Mitesh Suchak,Peter Bergstrom,John Riedl +4 more
- 22 Oct 1994
TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
6K
•Proceedings Article
Empirical analysis of predictive algorithms for collaborative filtering
John S. Breese,David Heckerman,Carl M. Kadie +2 more
- 24 Jul 1998
TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.