Discovering Causal Dependencies in Mobile Context-Aware Recommenders
Conference Proceeding Article
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.
Context acquisition, Context model, Restaurant recommender system
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
MDM 2006: 7th International Conference on Mobile Data Management: May 10-12, 2006, Nara, Japan
City or Country
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. 1-4. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/526