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@inproceedings{1662682,
author = {Elahi, Mehdi and El Ioini, Nabil and Lambrix, Anna and Ge, Mouzhi},
address = {Genoa, Italy},
booktitle = {Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization - UMAP 2020},
doi = {http://dx.doi.org/10.1145/3386392.3397590},
keywords = {collaborative filtering; personalized university ranking; preference elicitation; recommender systems; university recommendation},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Genoa, Italy},
isbn = {978-1-4503-6711-0},
pages = {6-10},
publisher = {ACM},
title = {Exploring Personalized University Ranking and Recommendation},
year = {2020}
}
TY - JOUR
ID - 1662682
AU - Elahi, Mehdi - El Ioini, Nabil - Lambrix, Anna - Ge, Mouzhi
PY - 2020
TI - Exploring Personalized University Ranking and Recommendation
PB - ACM
CY - Genoa, Italy
SN - 9781450367110
KW - collaborative filtering
KW - personalized university ranking
KW - preference elicitation
KW - recommender systems
KW - university recommendation
N2 - 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.
ER -
ELAHI, Mehdi, Nabil EL IOINI, Anna LAMBRIX a Mouzhi GE. Exploring Personalized University Ranking and Recommendation. Online. In \textit{Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization - UMAP 2020}. Genoa, Italy: ACM, 2020, s.~6-10. ISBN~978-1-4503-6711-0. Dostupné z: https://dx.doi.org/10.1145/3386392.3397590.
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