Ranking Users for Intelligent Message Addressing

Finding persons who are knowledgeable on a given topic (i.e. Expert Search) has become an active area of recent research [1–3]. In this paper we investigate the related task of Intelligent Message Addressing, i.e., finding persons who are potential recipients of a message under composition given its current contents, its previously-specified recipients or a few initial letters of the intended recipient contact (intelligent auto-completion). We begin by providing quantitative evidence, from a very large corpus, of how frequently email users are subject to message addressing problems. We then propose several techniques for this task, including adaptations of wellknown formal models of Expert Search. Surprisingly, a simple model based on the K-Nearest-Neighbors algorithm consistently outperformed all other methods. We also investigated combinations of the proposed methods using fusion techniques, which leaded to significant performance improvements over the baselines models. In auto-completion experiments, the proposed models also outperformed all standard baselines. Overall, the proposed techniques showed ranking performance of more than 0.5 in MRR over 5202 queries from 36 different email users, suggesting intelligent message addressing can be a welcome addition to email.