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» Explaining collaborative filtering recommendations
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ITRUST
2005
Springer
14 years 1 months ago
Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences
Collaborative Filtering (CF), the prevalent recommendation approach, has been successfully used to identify users that can be characterized as “similar” according to their logg...
Manos Papagelis, Dimitris Plexousakis, Themistokli...
EKAW
2006
Springer
13 years 11 months ago
Multilayered Semantic Social Network Modeling by Ontology-Based User Profiles Clustering: Application to Collaborative Filtering
We propose a multilayered semantic social network model that offers different views of common interests underlying a community of people. The applicability of the proposed model to...
Iván Cantador, Pablo Castells
GRC
2007
IEEE
13 years 8 months ago
Privacy Preserving Collaborative Filtering Using Data Obfuscation
Collaborative filtering (CF) systems are being widely used in E-commerce applications to provide recommendations to users regarding products that might be of interest to them. Th...
Rupa Parameswaran, Douglas M. Blough
SPIRE
2010
Springer
13 years 6 months ago
Fingerprinting Ratings for Collaborative Filtering - Theoretical and Empirical Analysis
Abstract. We consider fingerprinting methods for collaborative filtering (CF) systems. In general, CF systems show their real strength when supplied with enormous data sets. Earl...
Yoram Bachrach, Ralf Herbrich
ICTAI
2010
IEEE
13 years 5 months ago
From "I Like" to "I Prefer" in Collaborative Filtering
Collaborative filtering exploits user preferences, generally ratings, to provide them with recommendations. However, the ratings may not be completely trustworthy: the rating scale...
Armelle Brun, Ahmad Hamad, Olivier Buffet, Anne Bo...