A scalable collaborative filtering framework based on co-clustering

By Thomas George, Srujana Merugu. Industrial Conference on Data Mining, 2005

512 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/8e39f9ec05e058e727ece3067abb541f65c6b11e

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

PAC-Bayesian Analysis of Co-clustering and Beyond (2010, background): “…2004; Klugeret al., 2003; Cho and Dhillon, 2008), tokens and contexts in natural language processing (Freitag, 2004; Rohwer and Freitag, 2004; Li and Abe, 1998), viewers and movies in recommender systems(George and Merugu, 2005; Seldin et al., 2007; Salakhutdinov and Mnih, 2008; Seldin, 2009),etc.”
A generalized maximum entropy approach to bregman co-clustering and matrix approximation (2004, methodology): “Existing co-clustering techniques [5, 4, 8] achieve this by quantifying the \goodness" of a co-clustering in terms of the approximation error between the original”
Recommender systems survey (2013, methodology): “A different common approach uses clustering both for items and users (bi-clustering) [252,85].”
Comparison of collaborative filtering algorithms (2011, background): “12Overlapping clustering-based algorithms (a user could belong to several clusters) or co-clustering (grouping both users and items) [George and Merugu 2005; Shafiei and Milios 2006] offer an interpretation that is closer to reality.”
Introduction to Recommender Systems Handbook (2011, background): “These ideas have been remarkably well presented and discussed also by [33].”
Evaluating Recommendation Systems (2011, background): “Perhaps the most obvious example of such systems is the recommendation of news items or related stories in online newspapers [George, 2005].”
Improve collaborative filtering through bordered block diagonal form matrices (2013, methodology): “Some previous research has been performed attempting to address these problems, which mainly focus on various matrix-clustering [11, 26, 27, 39, 34] or community detection [6, 36, 23] techniques.”
Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering (2016, methodology): “Although co-clustering approaches have been used before in the collaborative filtering setting, the majority of those papers [13], [18], [44] is restricted to non-overlapping co-clusters.”
A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering (2010, unknown): “[22] consider a novel collaborative filtering approach based on a recently proposed weighted co-clustering algorithm that involves simultaneous clustering of users and items.”
Multi-Task Multi-View Clustering for Non-Negative Data (2015, methodology): “Co-clustering: Co-clustering has received a lot of attention in several practical applications such as text mining [Dhillon, 2001], genes [Cho et al., 2004] and recommender systems [George and Merugu, 2005].”
Transfer learning for collaborative filtering via a rating-matrix generative model (2009, methodology): “Several methods also aim at simultaneously clustering users and items for modeling rating patterns, such as the two-sided clustering model (Hofmann & Puzicha, 1999) and the co-clustering-based model (George & Merugu, 2005).”
Trust and nuanced profile similarity in online social networks (2009, methodology): “Coclustering methods that cluster both users and items have been shown to be effective and computationally ef.cient [George and Merugu 2005].”