ELAHI, Mehdi, Nabil EL IOINI, Anna LAMBRIX and Mouzhi GE. Exploring Personalized University Ranking and Recommendation. Online. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization - UMAP 2020. Genoa, Italy: ACM, 2020, p. 6-10. ISBN 978-1-4503-6711-0. Available from: https://dx.doi.org/10.1145/3386392.3397590.
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Basic information
Original name Exploring Personalized University Ranking and Recommendation
Authors ELAHI, Mehdi (578 Norway), Nabil EL IOINI (380 Italy), Anna LAMBRIX (380 Italy) and Mouzhi GE (156 China, guarantor, belonging to the institution).
Edition Genoa, Italy, Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization - UMAP 2020, p. 6-10, 5 pp. 2020.
Publisher ACM
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14330/20:00115805
Organization unit Faculty of Informatics
ISBN 978-1-4503-6711-0
Doi http://dx.doi.org/10.1145/3386392.3397590
Keywords in English collaborative filtering; personalized university ranking; preference elicitation; recommender systems; university recommendation
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 14/5/2021 06:39.
Abstract
Finding the right university to study is still a challenge for many people due to the large number of universities worldwide. Although there exist a number of global university rankings, they provide non# personalized rankings as one-size-fits-all solution. This becomes an issue since different people may have different preferences and considerations in mind, when choosing the university to study. This paper addresses this problem and presents a Recommender System to generate a personalized ranking list based on users particular preferences. The system is capable of eliciting users preferences, provided as ratings for universities, building predictive models on the preference data, and generating a personalized university ranking list that is tailored to the particular preferences and needs of the users. We performed two sets of experiments. First, we conducted an offline experiment using a dataset of user preferences, collected by the early version of our system. This allowed us to cross-validate and compare different recommender algorithms and choose the most accurate recommender algorithm that can better suit the particular problem at hand. We integrated the chosen algorithm in the final implementation of our system. As the follow-up, we performed a user study in order to analyze whether or not the final version of our system is usable from the perception of users. The results showed that the system has scored well above the benchmark and users assessed it as "good" in term of usability.
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