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Identifier |
000413379 |
Title |
Fairness in group recommendations in the health domain |
Alternative Title |
Δικαιοσύνη στις συστάσεις σε ομάδες στον τομέα της υγείας |
Author
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Στρατήγη, Μαρία Σ.
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Thesis advisor
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Πλεξουσάκης, Δημήτρης
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Abstract |
During the last decade, the number of users who look for health-related information
has impressively increased. On the other hand, health professionals have
less and less time to recommend useful sources of such information online to their
patients. To this direction, we target at streamlining the process of providing
useful online information to patients by their caregivers and improving as such
the opportunities that patients have to inform themselves online about diseases
and possible treatments. Using our system, relevant and high quality information
is delivered to patients based on their profile, as represented in their personal
healthcare record data, facilitating an easy interaction by minimizing the necessary
manual effort.
Specifically, in this work, we propose a model for group recommendations incorporating
fairness, following the collaborative filtering approach. As in collaborative
filtering, it is crucial to identify the correct set of similar users, in addition
to traditional methods, we pay particular attention on how to exploit user's health
information. To this direction, we define a novel similarity measure that is based
on the semantic distance between users' health problems. Our special focus is
on providing valuable suggestions to a caregiver who is responsible for a group of
users. We interpret valuable suggestions as ones that are both highly related and
fair to the users of the group. As such, we introduce in addition, a new aggregation
method incorporating fairness and we compare it with current state of the
art. Our experiments demonstrate the advantages of both the semantic similarity
method and the fair aggregation design. To the best of our knowledge, this is the
first work that introduces the concept of group recommendations incorporating
fairness in the health domain.
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Language |
English |
Subject |
Personal health profile |
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Προσωπικές πληροφορίες υγείας |
Issue date |
2017-11-24 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/8/b/8/metadata-dlib-1513686176-940585-4996.tkl
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Views |
367 |