Understanding and modeling conversations on microblogs

Recommender systems have proven to successfully influence user decisions on several applications and websites such as Amazon, eBay, Netflix, Spotify, among others. Largely, these systems rely on collaborative filtering to make useful recommendations but could suffer from cold start issues, i.e., lacking enough information for new users. By harnessing the popularity of social media (e.g. Twitter, Facebook) and other social media platforms, knowledge about users can be extracted, including intent of users that engage in conversations. This can provide additional information to applications using recommender systems in order to overcome issues like cold start. To that end, this research will tackle several challenges of natural language understanding in the context of conversations on social media platforms, such as: clustering, classification and summarization of conversations. Thus, the models developed will allow to extract knowledge from microblogs conversations that could be used in several applications.