@inproceedings{safavi2020toward,
author = {Safavi, Tara and Fourney, Adam and Sim, Robert and Juraszek,  Marcin and Williams, Shane and Friend, Ned and Koutra, Danai and Bennett, Paul},
title = {Toward Activity Discovery in the Personal Web},
organization = {ACM},
booktitle = {The 13th ACM International Conference on Web Search and Data Mining (WSDM)},
year = {2020},
month = {February},
abstract = {Individuals' personal information collections (their emails, files, appointments, web searches, contacts, etc) offer a wealth of insights into the organization and structure of their everyday lives. In this paper we address the task of learning representations of personal information items to capture individuals' ongoing activities, such as projects and tasks: Such representations can be used in activity-centric applications like personal assistants, email clients, and productivity tools to help people better manage their data and time.  We propose a graph-based approach that leverages the inherent interconnected structure of personal information collections, and derive efficient, exact techniques to incrementally update representations as new data arrive. We demonstrate the strengths of our graph-based representations against competitive baselines in a novel intrinsic rating task and an extrinsic recommendation task.},
url = {http://approjects.co.za/?big=en-us/research/publication/toward-activity-discovery-in-the-personal-web/},
}