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A social recommender system by combining social network and sentiment similarity: A case study of healthcare

机译：结合社交网络和情感相似性的社交推荐系统：医疗保健案例研究

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摘要

Social recommender systems aim to support user preferences and help users make better decisions in social media. The social network and the social context are two vital elements in social recommender systems. In this contribution, we propose a new framework for a social recommender system based on both network structure analysis and social context mining. Exponential random graph models (ERGMs) are able to capture and simulate the complex structure of a micro-blog network. We derive the prediction formula from ERGMs for recommending micro-blog users. Then, a primary recommendation list is created by analysing the micro-blog network structure. In the next step, we calculate the sentiment similarities of micro-blog users based on a sentiment feature set which is extracted from users' tweets. Sentiment similarities are used to filter the primary recommendation list and find users who have similar attitudes on the same topic. The goal of those two steps is to make the social recommender system much more precise and to satisfy users' psychological preferences. At the end, we use this new framework deal with big real-world data. The recommendation results of diabetes accounts of Weibo show that our method outperforms other social recommender systems.
机译：社交推荐系统旨在支持用户的偏好，并帮助用户在社交媒体中做出更好的决策。社交网络和社交环境是社交推荐系统中的两个至关重要的元素。在此贡献中，我们为基于网络结构分析和社交上下文挖掘的社交推荐系统提出了一个新框架。指数随机图模型（ERGM）能够捕获和模拟微博客网络的复杂结构。我们从ERGM推导了推荐微博用户的预测公式。然后，通过分析微博客网络结构来创建主要推荐列表。在下一步中，我们基于从用户推文中提取的情感特征集，计算微博用户的情感相似度。情感相似度用于过滤主要推荐列表并查找对同一主题持相似态度的用户。这两个步骤的目标是使社交推荐器系统更加精确，并满足用户的心理偏好。最后，我们使用这个新框架处理大型现实数据。微博的糖尿病推荐结果表明，我们的方法优于其他社会推荐系统。

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