Knowledge-based applications like recommender systems in social networks are powered by complex network of social discussions and user connections. Short text microblog platforms like Twitter are powerful in this aspect due to their real-time content dissemination as well as having a complex mesh of user connections. For example, users on Twitter tend to consume certain content to a greater or less extent depending on their interests over time. Quantifying this degree of content consumption in certain topics is an arduous task. This is further compounded by the amount of digital information that such platforms generate at any given time. Formulation of personalized user profiles based on user interests over time and friendship network is thus a problem. Therefore, user profiling based on their interests is important for personalized third-party content recommendations on the platform. In this paper we address this problem by presenting our solution in a two-step process:- (i) Firstly, we compute users’ Degree of Interest (DoI) towards a certain topic based on the overall users’ affinity towards that topic. (ii) Secondly, we affirm this DoI by correlating it to their friendship network. Furthermore, we describe our model for DoI computation and follow-back recommendation system by learning a low-dimensional vector representation of users and their disseminated content. This representation is used to train models for prediction of correct cluster classifications. In our experiments, we use a Twitter dataset to validate our approach by computing degrees of interest for certain test users in three diverse and generic topics. Experimental results show the effectiveness of our approach in the extraction of intra-user interests and better accuracy in follow-back recommendations with diversities in the topics.

基于知识的应用程序（如社交网络中的推荐系统）由社交讨论和用户连接的复杂网络提供支持。像Twitter这样的短文本微博平台在这方面非常强大，这是因为它们具有实时内容分发功能，并且具有复杂的用户连接网格。例如，Twitter上的用户往往会随着时间的流逝或多或少地消费某些内容。量化某些主题中内容消费的程度是一项艰巨的任务。这些平台在任何给定时间生成的数字信息量进一步加剧了这种情况。因此，基于随着时间的过去的用户兴趣和友谊网络来制定个性化用户简档是一个问题。因此，基于用户兴趣的用户配置文件对于平台上个性化的第三方内容推荐很重要。在本文中，我们通过两步介绍我们的解决方案来解决此问题：（i）首先，我们根据用户对该主题的总体亲和力来计算用户对该主题的兴趣度（DoI）。（ii）其次，我们通过将此DoI与他们的友谊网络相关联来确认该DoI。此外，我们通过学习用户及其分散内容的低维向量表示，描述了用于DoI计算和后续推荐系统的模型。该表示用于训练模型以预测正确的聚类分类。在我们的实验中，我们使用Twitter数据集通过在三个不同且通用的主题中计算某些测试用户的兴趣程度来验证我们的方法。实验结果表明，我们的方法在提取用户内部兴趣方面是有效的，并且在主题多样化的后续推荐中具有更高的准确性。