Rating Faculty for Foreign Student in Egypt by Bloom Filter Classifier and Collaborative Filtering
Mahmood A. Mahmood11, Tarek A. Mohamed2

1Mahmood A.Mahmood*, Department of Computer Science, Jouf University, Tubarjal, Kingdom of Saudi Arabia.
2Tarek A. Mohamed, Department of Information System Misr University for Science and Technology Faculty of Business.

Manuscript received on May 11, 2020. | Revised Manuscript received on May 21, 2020. | Manuscript published on May 30, 2020. | PP: 1741-1743 | Volume-9 Issue-1, May 2020. | Retrieval Number: A1267059120/2020©BEIESP | DOI: 10.35940/ijrte.A1267.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper presents an approach of bloom filter classifier and collaborative filtering to help foreign student to choose the suitable faculty according to his nationality and number of years that need to study. Our approach consist of three phases are: input phase, classification phase, and recommendation phase. In Input phase, the student enters the nationality and number of suggested years study. In classification phase, the approach classifies the student according to input data based on bloom filter classifier. In recommendation phase, the approach recommended the top five faculty if exists based on collaborative filtering technique (CF). Our dataset collected from Misr University for Science and Technology (MUST) and the results of our approach suitable and has a good manner for the student with accuracy 90%.
Keywords: Recommender Systems, Collaborative Filtering, Bloom Filter Classifier, Rating Technique.
Scope of the Article: Classification