Item-based Collaborative Filtering (CF) algorithms have been designed to deal with the scalability problems associated with traditional user-based CF approaches without sacrificin...
Classification fusion combines multiple classifications of data into a single classification solution of greater accuracy. Feature extraction aims to reduce the computational cost ...
Behrouz Minaei-Bidgoli, Gerd Kortemeyer, William F...
Collaborative Filtering (CF) recommendations are computed by leveraging a historical data set of users’ ratings for items. It assumes that the users’ previously recorded ratin...
Abstract: As enterprises worldwide race to improve real-time management to improve productivity, customer services and flexibility, huge resources have been invested into enterpris...
Experimental computer systems research typically ignores the end-user, modeling him, if at all, in overly simple ways. We argue that this (1) results in inadequate performance eva...
Peter A. Dinda, Gokhan Memik, Robert P. Dick, Bin ...