Enriching User Experience in Online Health Communities Through Thread Recommendations and Heterogeneous Information Network Mining

Abstract

Online health communities (OHCs) provide health consumers with platforms for discussing medical conditions and sharing a personal experience. Although a wealth of healthcare information is available in OHCs, consumers find it challenging to locate information of interest efficiently due to the information overload. The lack of medical knowledge and searching skills makes it even harder for consumers to retrieve demanded information from a popular OHC with hundreds of thousands of threads. Therefore, effective thread recommendation is critical for OHCs to enhance user experience and engage the users in the community. In this paper, we proposed to recommend threads to users in OHCs by exploiting heterogeneous healthcare information network mining. We first constructed a heterogeneous healthcare information network from OHCs data. Unlike bipartite graphs studied in most existing works, which only consider user nodes and item nodes, a heterogeneous healthcare information network retains the rich context information of users and threads. We extracted features from the network to capture basic network metrics, thread-thread relationship, and user-user relationship, and utilize the features to train a binary classification model for thread recommendation. Experiments were conducted using a data set collected from MedHelp. The proposed approach was proven to be effective in measuring user interests in online discussion threads. In addition, by testing our approaches using different settings, we found that the local similarity achieved better performance than the global similarity in heterogeneous information network. By incorporating thread- thread relationship and user-user relationship, it can achieve the best performance.