TY  - JOUR
T1  - An Automatic Arabic Web Personalization Search Engines and Information Retrieval Systems
AU - Hajeer, Safaa I. AU - Ismail, Rasha M. AU - Badr, Nagwa L. AU - Tolba, M.F. 
JO  - International Journal of Soft Computing
VL  - 11
IS  - 6
SP  - 382
EP  - 390
PY  - 2016
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2016.382.390
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2016.382.390
KW  - Information Retrieval (IR)
KW  -personalized search engines
KW  -automatic user profile
KW  -machine learning
KW  -semantic web ranking
KW  -ontology
KW  -Web Ontology Language (OWL)
KW  -personalized information
KW  -retrieval
KW  -collaborative filtering
KW  -contextual-based personalization
KW  -arabic language
AB  - Over the years, several achievements on the improvement of web personalized searching based on
user’s interests, preferences and contextual information have been made, unfortunately, most of them are
concerned with the static profile approach, preferences or weight values and not changed once the user
preference profile is created and this might be unacceptable by users. Also, they didn’t consider usage features
like the changing in user’s attributes, ontology, description and features over time. In addition, its rare that any
of them are considered in Arabic Language Personalization Re-ranking through the search engines. Therefore,
in this study we proposed a New Automatic Semantic Personalization Re-ranking (NASPR) approach. The
objective of this NASPR algorithm is to overcome the drawbacks of ranking algorithms and improve the
efficiency of web searching. The NASPR approach was applied to 242 Arabic Corpus to measure its
performance and the results show improvements in the recall and precision by using the new personalized
approach.
ER  -