Personalized content recommendation in online health communities

Abstract

PurposeRecommending suitable content for users of online health communities (OHCs) is critical for overcoming information overload problem and facilitate medical decision making, but remains not fully investigated. This study aims to provide a content recommendation approach to automatically match valuable health-related information for OHC members.Design/methodology/approachA framework of health-related content recommendation was proposed by leveraging rich social information in online communities. The authors constructed user influence relationship (UIR) utilizing users' interaction records, user profiles and user-generated content. The initial user rating matrix and the user post matching matrix were then created by analyzing text content of posts. Finally, the user rating matrix and the recommended content were generated for community members. Datasets were collected from an OHC to evaluate the effectiveness of the proposed approach.FindingsThe experimental results revealed that the proposed method statistically outperformed baseline models in content recommendation for users of OHCs.Research limitations/implicationsThe incorporation of social information can significantly enhance the performance of content recommendation in OHCs. The user post matching degree based on text analysis can improve the effectiveness of recommendation.Practical implicationsThis study potentially contributes to the social support exchange and medical decision making of community members and the sustainable prosperity of OHCs.Originality/valueThis study proposes a novel social content recommendation method for online health consumers based on UIRs by leveraging social information in OHCs. The results indicate the significance of social information in content recommendation of healthcare social media.

How doctor image features engage health science short video viewers? Investigating the age and gender bias

“…Moreover, viewer engagement with videos is associated with their purpose of watching. For health science short-form videos, most viewers are seeking health-related information, while some patients look for qualified physicians, alternative treatment plans, or social support instead of entertainment (Oh et al, 2016;Yang and Gao, 2021) These differences in viewer purposes may distinguish the influencing mechanisms of health science videos from those of other short-form videos. Therefore, it is crucial to understand the influencing mechanism of health science videos.…”

“…Health science videos with humor may fulfill viewers' hedonic needs as well (De Choudhury et al, 2014). For patient viewers, health science videos serve as a platform for them to interact with experts and other patients in the chatting area below the video (Oh et al, 2016;Yang and Gao, 2021).…”

“…For patient viewers, health science videos serve as a platform for them to interact with experts and other patients in the chatting area below the video (Oh et al. , 2016; Yang and Gao, 2021).…”

A Personalized Course Resource Recommendation Method Based on Deep Learning in an Online Multi-Modal Multimedia Education Cloud Platform

“…The traditional recommendation methods cannot effectively use the auxiliary information and its potential relationship, resulting in a cold start, sparse data, and other problems. With the growth of data volume and the improvement of computing power, the combination of deep learning and course recommendation provides new ideas for solving the above problems (Yang & Gao, 2022;Nitu et al, 2021). The research of deep learning methods for personalized course recommendation has become the focus of current education research.…”