Calendar-Aware Proactive Email Recommendation - Zhao, Qian; Bennett, Paul; Fourney, Adam; Thompson, Anne Loomis; Williams, Shane; Troy, Adam D.; Dumais, Susan (Proceedings of the 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), 2018)
In this paper, we study how to leverage calendar information to help with email re-finding using a zero-query prototype, Calendar Aware Proactive Email Recommender System(CAPERS). CAPERS proactively selects and displays potentially useful emails to users based on their upcoming calendar events with a particular focus on meeting preparation. We approach this problem domain through a survey, a task-based experiment, and a field experiment comparing multiple email recommenders in a large technology company. We first show that a large proportion of email access is related to meetings and then study the effects of four email recommenders on user perception and engagement taking into account four categories of factors: the amount of email content, email recency, calendar-email content match, and calendar-email people match. We demonstrate that these factors all positively predict the usefulness of emails to meeting preparation and that calendar-email content match is the most important. We study the effects of different machine learning models for predicting usefulness and find that an online-learned linear model doubles user engagement compared with the baselines, which suggests the benefit of continuous online learning.