03132cam a2200421 a 4500 EG-GiCUC 20260513123105.0 220221s2022 ua a frm 000 0 eng d EG-GiCUC EG-GiCUC eng eng eng ara Deposite 600 600 21 M.Sc Cai01.20.04.M.Sc.2022.Di.I Dina Mohamed Kamal Atito preparation. Improving recommendation systems using semantic technologies / Dina Mohamed Kamal Atito ; Supervised Hoda Mokhtar Omar Mokhtar , Ayman Ramadan Elkilany. تحسين أنظمة التوصية بإستخدام التكنولوجيا الدلالية 2022 65 pages : illustrations ; 30 cm+ CD text rda content Unmediated rdamedia volume rdacarrier Thesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial intelligence - Department of Information Systems Bibliography: pages 69-75. Recommendation 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 Issued also as CD Text in English and abstract in Arabic & English. LDA Latent Dirichlet Allocation(LDA) Recommendation Systems Word2vec Ayman Ramadan Elkilany thesis advisor. Hoda Mokhtar Omar Mokhtar thesis advisor. 01-01-2022 Hoda Mokhtar Omar Mokhtar Ayman Ramadan Elkilany Cairo University Faculty of Computers and Artificial intelligence Department of Information Systems Nazla Revisor Shimaa Cataloger ddc TH 84293 ddc 148238 CUCL cl210 2024-02-11 85462 Cai01.20.04.M.Sc.2022.Di.I 01010110085462000 2023-09-22 00:00:00 2023-09-22 TH ddc 148239 CUCL cl2u8 2024-02-11 13818004 Cai01.20.04.M.Sc.2022.Di.I 01020110085462000 2023-09-22 00:00:00 85462.CD 2023-09-22 CD