---
title: "Efficient Online Learning and Prediction of Users' Desktop Actions"
authors:
  - "Omid Madani"
  - "Hung Hai Bui"
  - "Eric Yeh"
doi: ""
url: "https://ijcai.org/proceedings/2009/244"
pdf: "https://ijcai.org/proceedings/2009/0244.pdf"
venue: "Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence"
year: 2009
pages: "1457-1461"
track: ""
keywords:
---

# Efficient Online Learning and Prediction of Users' Desktop Actions

Omid Madani, Hung Hai Bui, Eric Yeh

*Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence*, pages 1457-1461.

## Abstract

We investigate prediction of users' desktop activities in the Unix domain. The learning techniques we explore do not require explicit user teaching. We show that simple efficient many-class learning can perform well for action prediction, significantly improving over previously published results and baselines. This finding is promising for various human-computer interaction scenarios where a rich set of potentially predictive features is available, where there can be many different actions to predict, and where there can be considerable nonstationarity.Omid Madani, Hung Bui, Eric Yeh