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» Recommendation Diversification Using Explanations
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CHI
2008
ACM
14 years 6 months ago
PeerChooser: visual interactive recommendation
Collaborative filtering (CF) has been successfully deployed over the years to compute predictions on items based on a user's correlation with a set of peers. The black-box na...
Barry Smyth, Brynjar Gretarsson, John O'Donovan, S...
RECSYS
2009
ACM
14 years 1 days ago
Generating transparent, steerable recommendations from textual descriptions of items
We propose a recommendation technique that works by collecting text descriptions of items and using this textual aura to compute the similarity between items using techniques draw...
Stephen J. Green, Paul Lamere, Jeffrey Alexander, ...
ICDM
2008
IEEE
183views Data Mining» more  ICDM 2008»
13 years 12 months ago
Collaborative Filtering for Implicit Feedback Datasets
A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track differe...
Yifan Hu, Yehuda Koren, Chris Volinsky
SEMWEB
2005
Springer
13 years 11 months ago
Debugging OWL-DL Ontologies: A Heuristic Approach
Abstract. After becoming a W3C Recommendation, OWL is becoming increasingly widely accepted and used. However most people still find it difficult to create and use OWL ontologies...
Hai Wang, Matthew Horridge, Alan L. Rector, Nick D...
UAI
2003
13 years 6 months ago
Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes
Collaborative filtering (CF) and contentbased filtering (CBF) have widely been used in information filtering applications, both approaches having their individual strengths and...
Kai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying...