Publication Type

Conference Proceeding Article

Version

Postprint

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

Research Areas

Data Management and Analytics

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

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1109/MDM.2006.72

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