TY  - JOUR
T1  - Improving Recommendation System Based on Homophily
Principle and Demographic
AU - Khairallah, Zainab AU - Nawaf, Huda Naji 
JO  - Research Journal of Applied Sciences
VL  - 11
IS  - 10
SP  - 1102
EP  - 1106
PY  - 2016
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2016.1102.1106
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2016.1102.1106
KW  - Collaborative filtering
KW  -homophily
KW  -Naive Bayes classifier
KW  -demographic
KW  -clustering
KW  -K-medoids
AB  - Collaborative filtering is one of the prevalent successful approaches in the Recommender systems to predicate items to users based on rating matrix and mitigate the difficulty of finding interesting things on the spider’s web. In this paper, we percent a Naïve Bayes model by taking into account the similarity in preferences (homophily) among the users and attributes of users (demographic) as a prior knowledge to enhance the prediction accuracy of collaborative filtering. Experiments are implemented on Movielens datasets 100K and 1M. The results show that the system can provide a recommendation in a best manner.
ER  -