Discovering Causal Dependencies in Mobile Context-Aware Recommenders

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 mec...

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Bibliographic Details
Main Authors: Yap, Ghim-Eng, Tan, Ah-Hwee, Pang, Hwee-Hwa
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Context
Context-aware services
Engineering management
Information management
Management information systems
Mobile computing
Recommender systems
Technology management
Uncertainty
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Description
Summary: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.
ISSN:1551-6245
2375-0324
DOI:10.1109/MDM.2006.72