Paper 58 (Research track)

FairGRecs: Fair Group Recommendations by Exploiting Personal Health Information & Semantics

Author(s): Maria Stratigi, Haridimos Kondylakis, Kostas Stefanidis

Abstract: Nowadays, the number of people who search for information related
to health has significantly increased, while the time of health professionals
for recommending online useful sources of information has
been reduced to a great extend. FairGRecs aims to offer an effective
approach that provides valuable information to users, in the form
of suggestions, via their caregivers, and improve as such the opportunities
that users have to inform themselves online about health
problems and possible treatments. Specifically, we propose a model
for group recommendations incorporating the notion of fairness,
following the collaborative filtering approach. For computing similarities
between users, we define a novel 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 a new aggregation design, incorporating fairness,
and we compare it with current state-of-the-art. Our experiments
demonstrate the advantages of both the semantic similarity measure
and the fair aggregation design.

Keywords: Group Recommendations; Semantic Recommendations; Collaborative Filtering

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