Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
5-2006
Abstract
Mobile context-aware recommender systems face unique challenges in acquiring context. Resource limitations make minimizing context acquisition a practical need, while the uncertainty inherent to the mobile environment makes missing context values a major concern. This paper introduces a scalable mechanism based on Bayesian network learning in a tiered context model to overcome both of these challenges. Extensive experiments on a restaurant recommender system showed that our mechanism can accurately discover causal dependencies among context, thereby enabling the effective identification of the minimal set of important context for a specific user and task, as well as providing highly accurate recommendations even when context values are missing.
Keywords
Context acquisition, Context model, Restaurant recommender system
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
MDM 2006: 7th International Conference on Mobile Data Management: May 10-12, 2006, Nara, Japan
First Page
1630540-1
Last Page
1630540-4
ISBN
9781424429455
Identifier
10.1109/MDM.2006.72
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
YAP, Ghim-Eng; TAN, Ah-Hwee; and PANG, Hwee Hwa.
Discovering Causal Dependencies in Mobile Context-Aware Recommenders. (2006). MDM 2006: 7th International Conference on Mobile Data Management: May 10-12, 2006, Nara, Japan. 1630540-1-1630540-4.
Available at: https://ink.library.smu.edu.sg/sis_research/526
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/MDM.2006.72
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons