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» Providing Justifications in Recommender Systems
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WWW
2008
ACM
14 years 8 months ago
A Study of User Profile Generation from Folksonomies
Recommendation systems which aim at providing relevant information to users are becoming more and more important and desirable due to the enormous amount of information available ...
Ching-man Au Yeung, Nicholas Gibbins, Nigel Shadbo...
KDD
2012
ACM
187views Data Mining» more  KDD 2012»
11 years 10 months ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
ACL
2011
12 years 11 months ago
Better Hypothesis Testing for Statistical Machine Translation: Controlling for Optimizer Instability
In statistical machine translation, a researcher seeks to determine whether some innovation (e.g., a new feature, model, or inference algorithm) improves translation quality in co...
Jonathan H. Clark, Chris Dyer, Alon Lavie, Noah A....
KDD
2008
ACM
155views Data Mining» more  KDD 2008»
14 years 8 months ago
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are ana...
Yehuda Koren
WEBI
2009
Springer
14 years 2 months ago
Specialized Review Selection for Feature Rating Estimation
—On participatory Websites, users provide opinions about products, with both overall ratings and textual reviews. In this paper, we propose an approach to accurately estimate fea...
Chong Long, Jie Zhang, Minlie Huang, Xiaoyan Zhu, ...