Personalized Broadcast Message Prioritization

A broadcast message is defined as a message that can be sent to a group of subscribers at once. Popular types of broadcast messages include broadcast emails, tweets, broadcast short messages and so on. With billions of broadcast messages sent and received every day, it is fundamentally impacting the way people live, work and communicate. However, there comes a curse with broadcast messages, the broadcast message overload problem. Broadcast message overload refers to the situation in which the majority of broadcast messages are usually unimportant or irrelevant and people have to waste a large amount of time handling them, causing a trillion-level economy loss in productivity. The serious situation leads to a thriving research field, personalized broadcast message prioritization, which aims to predict the importance label for broadcast messages and help users to ease the burden of handling unimportant broadcast messages. In this thesis, we work on three broadcast message prioritization related research questions focusing on two popular types of broadcast messages, broadcast emails, and tweets. First, we work on the mention recommendation problem in micro-blogging systems, which is highly related to the tweet prioritization task. Mention recommendation tries to recommend the optimal set of users to be mentioned in a tweet in order to expand its diffusion. Considering tweet prioritization and user influence at the same time, we propose the first framework to handle the mention recommendation problem by designing a new learning to rank model. In our second research question, we focus directly on the personalized broadcast email prioritization task. We proposed the first broadcast email prioritization framework that adopts the paradigm of collaborative filtering. To overcome the complete cold start item challenge, a novel active learning framework is proposed, considering unique challenges, like the one-class implicit feedback and time-sensitive feedback. In the third research question, we continue to work on the broadcast email prioritization problem while considering the fact that there exist large numbers of mailing lists in a real email system. A cross-domain recommendation framework is proposed to transfer extra knowledge from other similar mailing lists, which helps to provide better predictions for newly enrolled users and new mailing lists.