The abundance of user generated content on social media provides the opportunity to build models that are able to accurately and effectively extract, mine and predict users’ interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered to be intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users’ interests and preferences. In this monograph, we will cover five important subjects related to the mining of user interests from social media: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and opportunities for further work.

用户在社交媒体上生成的大量内容为构建模型提供了机会，这些模型能够准确有效地提取，挖掘和预测用户的兴趣，从而希望能够实现更有效的用户参与，更优质的适当服务交付和更高的用户满意度。尽管用于建立用户资料的传统方法依赖于基于AI的偏好启发技术，该技术可能被用户认为是侵入性的并且是用户所不希望的，但最近的进展集中在确定用户兴趣和偏好的非侵入性但准确的方法上。在本专题中，我们将涵盖与从社交媒体挖掘用户兴趣相关的五个重要主题：（1）社交用户兴趣建模的基础，例如信息源，