TY  - BOOK
AU  - Mahmoud Abdelmoneim Mahmood
AU  - Aboul Ella Hassanien , 
AU  - Hesham Ahmed Hefny , 
AU  - Nashwa Elbendary , 
TI  - An intelligent multi-agent recommender system / 
PY  - 2014///
CY  - Cairo : 
PB  - Mahmoud Abdelmoneim Mahmood , 
KW  - Information systems
KW  - Multi-Agent
KW  - Recommender System
N1  - Thesis (Ph.D.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Sciences; Issued also as CD
N2  - For many users of information systems, 3information overload3 has become a problem: The amount of information they must sift through has reached the point where it is  overwhelming. Recommender systems are intelligent tools that help on-line users to tame information overload. Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a user{u2019}s needs based on data coming from similar users, are becoming increasingly popular as ways to combat this information overload. While accuracy has been a major focus of CF, in practice, efficiency, data sparsity, scalability, and cold start problem are also important issues in CF in order to enhance  the performance of CF systems. Efficiency refers to the cost of CF algorithms. We propose an approach that is able to predict what information will meet a user{u2019}s needs based on data coming from similar users with accepted accuracy to user
ER  -