Helper Recommendation with seniority control in Online Health Community

TL;DR

This paper introduces MINT, a novel variational autoencoder-based model designed to improve helper recommendations in online health communities by considering seniority and disentangling factors influencing social support.

Contribution

It proposes a new recommendation model that addresses the unique challenges of social support prediction in OHCs, incorporating seniority control and disentanglement techniques.

Findings

MINT effectively models helper-seeker relationships in OHCs.

The model improves recommendation accuracy over baseline methods.

It enhances the quality of social support in online health communities.

Abstract

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.