TY  - JOUR
T1  - Collaborative Filtering Recommendation using Personalized Page Rank
Algorithm with New Personalized Parameters
AU - M. Naji, Hayder AU - A. Al-Sultany, Ghaidaa 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 16
SP  - 4108
EP  - 4112
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.4108.4112
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.4108.4112
KW  - collaborative filtering
KW  -personalized parameters
KW  -personalized page rank
KW  -Recommendation system
KW  -accurate
AB  - Collaborative filtering recommendation system shares the user’s interests and recommends items to
a user based on the interests of the other users whom are similar to his/her owntendencies. Basically, the
Personalized Page Rank Algorithm (PPR) suggests items with respect to the target user by personalizing him/her
only. In this study, Iteratively with each target user, the remaining users are personalized according to their
rating patterns by supporting them withnew Personalized Parameters (PP). The personalized parameters have
a role of personalized measure from which each user’s rank will affect and be affected on the other
user’s ranks depending on the PP values. The achievement of more accurate recommender system needsmore
personalization to satisfy user’s tendencies so we Present a Personalized Recommendation system using PPR
algorithm with more personalization method. Finally, classification accuracy measures have been used to
evaluate the outcome top-N recommendation list on a MovieLens dataset in comparison with the outcome of
traditional PPR.
ER  -