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ICDM
2003
IEEE
142views Data Mining» more  ICDM 2003»
14 years 2 months ago
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
Collaborative Filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. E-commerce sites use CF systems to suggest products to customers base...
Huseyin Polat, Wenliang Du
SIGIR
2011
ACM
12 years 12 months ago
Collaborative competitive filtering: learning recommender using context of user choice
While a user’s preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learni...
Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hon...
SIGIR
2009
ACM
14 years 3 months ago
Temporal collaborative filtering with adaptive neighbourhoods
Recommender Systems, based on collaborative filtering (CF), aim to accurately predict user tastes, by minimising the mean error achieved on hidden test sets of user ratings, afte...
Neal Lathia, Stephen Hailes, Licia Capra
AMM
2011
118views more  AMM 2011»
13 years 4 months ago
From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering
—The number of resources or items that users can now access when navigating on the Web or using e-services, is so huge that these might feel lost due to the presence of too much ...
Armelle Brun, Sylvain Castagnos, Anne Boyer
ICML
2004
IEEE
14 years 9 months ago
The multiple multiplicative factor model for collaborative filtering
We describe a class of causal, discrete latent variable models called Multiple Multiplicative Factor models (MMFs). A data vector is represented in the latent space as a vector of...
Benjamin M. Marlin, Richard S. Zemel