Online Recommender System Considering Changes in User's Preference

J. Hamidzadeh; M. Moradi

Volume 9, Issue 2 , April 2021, , Pages 203-212

http://dx.doi.org/10.22044/jadm.2020.9518.2085

Abstract
  Recommender systems extract unseen information for predicting the next preferences. Most of these systems use additional information such as demographic data and previous users' ratings to predict users' preferences but rarely have used sequential information. In streaming recommender systems, the emergence ...  Read More

H.6.3.1. Classifier design and evaluation
Ensemble-based Top-k Recommender System Considering Incomplete Data

M. Moradi; J. Hamidzadeh

Volume 7, Issue 3 , July 2019, , Pages 393-402

http://dx.doi.org/10.22044/jadm.2019.7026.1830

Abstract
  Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user-interest (Top-k recommendation problem). Demanding sufficient ...  Read More

H.6. Pattern Recognition
IRDDS: Instance reduction based on Distance-based decision surface

J. Hamidzadeh

Volume 3, Issue 2 , July 2015, , Pages 121-130

http://dx.doi.org/10.5829/idosi.JAIDM.2015.03.02.01

Abstract
  In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, ...  Read More