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.

翻译：在线健康社区（OHC）是患者交流相似经历、提供情感支持的论坛。OHC中的社会支持在缓解和康复患者方面发挥着关键作用。然而，由于帖子数量众多且患者访问的随机性，许多患者提出的时效性问题往往得不到回答。为解决这一问题，亟需提出一种推荐系统来帮助问题寻求者找到合适的帮助者。然而，开发增强OHC中社会支持的推荐算法仍是一个未被充分探索的领域。传统推荐系统无法直接适用，原因如下：第一，与传统推荐系统中用户-物品链接不同，OHC中帮助者-寻求者链接的形成基于多种异质原因，难以建模其背后的社会支持；第二，区分历史活动对患者特征的影响较为困难；第三，确保推荐帮助者具备足够专业知识以协助寻求者具有显著挑战性。为应对上述挑战，我们开发了一种单调正则化解耦变分自编码器（MINT）模型，以增强OHC中的社会支持。