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Online health communities (OHCs) are forums where patients with similarconditions communicate their experiences and provide moral support. Socialsupport in OHCs plays a crucial role in easing and rehabilitating patients.However, many time-sensitive questions from patients often remain unanswereddue to the multitude of threads and the random nature of patient visits inOHCs. To address this issue, it is imperative to propose a recommender systemthat assists solution seekers in finding appropriate problem helpers.Nevertheless, developing a recommendation algorithm to enhance social supportin OHCs remains an under-explored area. Traditional recommender systems cannotbe directly adapted due to the following obstacles. First, unlike user-itemlinks in traditional recommender systems, it is hard to model the socialsupport behind helper-seeker links in OHCs since they are formed based onvarious heterogeneous reasons. Second, it is difficult to distinguish theimpact of historical activities in characterizing patients. Third, it issignificantly challenging to ensure that the recommended helpers possesssufficient expertise to assist the seekers. To tackle the aforementionedchallenges, we develop a Monotonically regularIzed diseNTangled VariationalAutoencoders (MINT) model to strengthen social support in OHCs.

Helper Recommendation with seniority control in Online Health Community