Name
Abstract | ||
|---|---|---|
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.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3209978.3210001 | SIGIR |
Keywords | Field | DocType |
email recommendation,email re-finding,email search | Recommender system,Online learning,Information retrieval,Email search,Problem domain,Computer science,Linear model,User engagement,Perception | Conference |
ISBN | Citations | PageRank |
978-1-4503-5657-2 | 0 | 0.34 |
References | Authors | |
25 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qian Zhao | 1 | 70 | 9.66 |
| Paul N. Bennett | 2 | 1500 | 87.93 |
| Adam Fourney | 3 | 64 | 8.18 |
| Anne Loomis Thompson | 4 | 5 | 2.22 |
| Shane Williams | 5 | 27 | 3.28 |
| Adam Troy | 6 | 1 | 0.70 |
| Susan Dumais | 7 | 13948 | 2130.47 |