02674cam a2200301 a 4500003000900000005001700009008004100026040002800067041001800095082000800113100004300121245015900164246010200323264001000425300004700435336002200482337002500504338002300529502012500552504003100677520141400708530002202122546005402144650000802198653007302206700004502279700004802324EG-GiCUC20260513123105.0220221s2022    ua a   frm    000 0 eng d  aEG-GiCUCcEG-GiCUCbeng0 aengbengbara04a6000 aDina Mohamed Kamal Atitoepreparation.10aImproving recommendation systems using semantic technologies / cDina Mohamed Kamal Atito ; Supervised Hoda Mokhtar Omar Mokhtar , Ayman Ramadan Elkilany.15aتحسين أنظمة التوصية بإستخدام التكنولوجيا الدلالية 0c 2022  a65 pages : billustrations ; c30 cm+ eCD  atext2rda content  aUnmediated2rdamedia  avolume2rdacarrier  aThesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial intelligence - Department of Information Systems  aBibliography: pages 69-75. 3aRecommendation systems are algorithms that aim to predict the users' needs and automatically suggest the most relevant items to the users. Recommender systems are becoming increasingly popular in our daily lives and applied in different domains to facilitate finding relevant and interesting items to the users. In the academic domain, the academic article recommendation systems have gained a lot of interest as an effective tool to suggest relevant articles for researchers according to their interests. An explicit identification of the topics of interest from the contents of academic articles that the researchers have authored, downloaded, or read has been always a challenging task. Accurate articles recommendation relies on the true identification of researchers{u2018} interests which is affected by the quality of the article's textual representation. In this thesis, we aim to improve the results of the academic recommendation system by enhancing the representation of the article and consequently enhancing the quality of the recommendation. In order to improve the representation of the articles, we focus on the semantic approaches to represent the words' semantic meanings rather than their syntactic representation only. In this thesis, two semantic representation models are proposed for articles representation, both models have been applied in the academic articles recommendation process  aIssued also as CD  aText in English and abstract in Arabic & English. 0aLDA 4aLatent Dirichlet Allocation(LDA) aRecommendation SystemsaWord2vec 0 aAyman Ramadan Elkilany ethesis advisor.0 aHoda Mokhtar Omar Mokhtar ethesis advisor.