TY  - JOUR
T1  - Influential Nodes Based Alleviation of User Cold-Start
Problem in Recommendation System
AU - Hussein, Mohsin Hasan AU - Nawaf, Huda Naji AU - Bhaya, Wesam 
JO  - Research Journal of Applied Sciences
VL  - 11
IS  - 10
SP  - 1107
EP  - 1114
PY  - 2016
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2016.1107.1114
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2016.1107.1114
KW  - Cold start
KW  -influential nodes
KW  -centrality
KW  -recommendation system
KW  -social relations
KW  -cold-start problem
AB  - The Cold-start problem considered as one of the most common limitations of Recommender Systems (RSs) which indicates to situation when a new user or items lastly combined to the system. We focus in this study on set of the users who did not receive attention by researchers about their preferences in future, so they often have been ignored. Highlight on their interests may improve the recommendation systems; we utilize the social relation information and social network analysis to alleviating the new user’s problem by exploiting the preferences of influential nodes to recommend items for new users. So, the main contribution in this research is employing the influential nodes to alleviate the cold start problem. Top-M influential nodes have been assigned from the social communities using closeness and degree centrality measure and then suggest the most popular interest items of those nodes to new users. The performance of our approach has been applied using two data sets: Lastfm published by Group lens research and CiaoDVD published by LibRec research group. The experimental results show that exploiting influential nodes in cold start issue improve the recommendation system.
ER  -