Enriching User Experience in Online Health Communities Through Thread Recommendations and Heterogeneous Information Network Mining
Enriching User Experience in Online Health Communities Through Thread Recommendations and Heterogeneous Information Network Mining
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DOI：
10.1109/tcss.2018.2879044
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发表时间：
2018-12
影响因子：
5
通讯作者：
Christopher C. Yang;Ling Jiang
中科院分区：
文献类型：
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作者：
Christopher C. Yang;Ling Jiang
在线健康社区（OHC）为健康消费者提供了讨论医疗状况和分享个人经验的平台。尽管在OHC中可以获得丰富的医疗保健信息，但是由于信息过载，消费者发现有效地定位感兴趣的信息是具有挑战性的。缺乏医学知识和搜索技能使得消费者更难从拥有数十万线程的流行OHC中检索所需信息。因此，有效的线程推荐对于OHC增强用户体验和吸引社区中的用户至关重要。在本文中，我们提出了建议线程用户在OHC利用异构医疗信息网络挖掘。我们首先从OHCs数据构建了一个异构的医疗信息网络。与大多数现有作品中研究的二分图不同，这些作品只考虑用户节点和项目节点，异构医疗信息网络保留了用户和线程的丰富上下文信息。我们从网络中提取特征来捕获基本的网络度量、线程-线程关系和用户-用户关系，并利用这些特征来训练用于线程推荐的二进制分类模型。使用从MedHelp收集的数据集进行实验。所提出的方法被证明是有效的测量用户的兴趣在线讨论线程。此外，通过测试我们的方法使用不同的设置，我们发现，局部相似性取得了更好的性能比全局相似性在异构信息网络。通过结合线程-线程关系和用户-用户关系，它可以达到最佳性能。
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.