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» Accuracy in Rating and Recommending Item Features
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AAIM
2008
Springer
208views Algorithms» more  AAIM 2008»
13 years 10 months ago
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
Many recommendation systems suggest items to users by utilizing the techniques of collaborative filtering (CF) based on historical records of items that the users have viewed, purc...
Yunhong Zhou, Dennis M. Wilkinson, Robert Schreibe...
RECSYS
2010
ACM
13 years 8 months ago
Collaborative filtering via euclidean embedding
Recommendation systems suggest items based on user preferences. Collaborative filtering is a popular approach in which recommending is based on the rating history of the system. O...
Mohammad Khoshneshin, W. Nick Street
ICML
1998
IEEE
14 years 8 months ago
Learning Collaborative Information Filters
Predicting items a user would like on the basis of other users' ratings for these items has become a well-established strategy adopted by many recommendation services on the ...
Daniel Billsus, Michael J. Pazzani
ICDM
2007
IEEE
147views Data Mining» more  ICDM 2007»
14 years 2 months ago
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships. A predominant approach to collabo...
Robert M. Bell, Yehuda Koren
IBPRIA
2009
Springer
13 years 11 months ago
Textural Features for Hyperspectral Pixel Classification
Hyperspectral remote sensing provides data in large amounts from a wide range of wavelengths in the spectrum and the possibility of distinguish subtle differences in the image. For...
Olga Rajadell, Pedro García-Sevilla, Filibe...