Learning and predicting multimodal daily life patterns from cell phones

Farrahi, Katayoun and Gatica-Perez, Daniel (2009) Learning and predicting multimodal daily life patterns from cell phones. In ICMI-MLMI'09 - Proceedings of the International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interfaces. Association for Computing Machinery. pp. 277-280 . (doi:10.1145/1647314.1647373).

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we investigate the multimodal nature of cell phone data in terms of discovering recurrent and rich patterns in people's lives. We present a method that can discover routines from multiple modalities (location and proximity) jointly modeled, and that uses these informative routines to predict unlabeled or missing data. Using a joint representation of location and proximity data over approximately 10 months of 97 individuals' lives, Latent Dirichlet Allocation is applied for the unsupervised learning of topics describing people's most common locations jointly with the most common types of interactions at these locations. We further successfully predict where and with how many other individuals users will be, for people with both highly and lowly varying lifestyles.

This record has no associated files available for download.

More information

Published date: 1 November 2009

Venue - Dates: International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interfaces, ICMI-MLMI'09, , Cambridge, MA, United States, 2009-11-02 - 2009-11-06

Keywords: Data prediction, Mobile phone data, Multi-modal data, Reality mining, Topic models

Learn more about Vision, Learning and Control research