An intelligent multi-agent recommender system /
Mahmoud Abdelmoneim Mahmood
An intelligent multi-agent recommender system / نظام توصية ذكى متعدد الوكلاء Mahmoud Abdelmoneim Mahmood ; Supervised Aboulella Hassanien , Hesham Ahmed Hefny , Nashwa Elbendary - Cairo : Mahmoud Abdelmoneim Mahmood , 2014 - 132 P. : charts ; 30cm
Thesis (Ph.D.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Sciences
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 users 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 users needs based on data coming from similar users with accepted accuracy to user
Information systems Multi-Agent Recommender System
An intelligent multi-agent recommender system / نظام توصية ذكى متعدد الوكلاء Mahmoud Abdelmoneim Mahmood ; Supervised Aboulella Hassanien , Hesham Ahmed Hefny , Nashwa Elbendary - Cairo : Mahmoud Abdelmoneim Mahmood , 2014 - 132 P. : charts ; 30cm
Thesis (Ph.D.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Sciences
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 users 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 users needs based on data coming from similar users with accepted accuracy to user
Information systems Multi-Agent Recommender System