3 research outputs found
Helper Recommendation with seniority control in Online Health Community
Online health communities (OHCs) are forums where patients with similar
conditions communicate their experiences and provide moral support. Social
support in OHCs plays a crucial role in easing and rehabilitating patients.
However, many time-sensitive questions from patients often remain unanswered
due to the multitude of threads and the random nature of patient visits in
OHCs. To address this issue, it is imperative to propose a recommender system
that assists solution seekers in finding appropriate problem helpers.
Nevertheless, developing a recommendation algorithm to enhance social support
in OHCs remains an under-explored area. Traditional recommender systems cannot
be directly adapted due to the following obstacles. First, unlike user-item
links in traditional recommender systems, it is hard to model the social
support behind helper-seeker links in OHCs since they are formed based on
various heterogeneous reasons. Second, it is difficult to distinguish the
impact of historical activities in characterizing patients. Third, it is
significantly challenging to ensure that the recommended helpers possess
sufficient expertise to assist the seekers. To tackle the aforementioned
challenges, we develop a Monotonically regularIzed diseNTangled Variational
Autoencoders (MINT) model to strengthen social support in OHCs