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Discovering Causal Dependencies in Mobile Context-Aware Recommenders

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3 Author(s)
Yap, Ghim-Eng ; Nanyang Technological University, Singapore ; Ah-Hwee Tan ; Pang, Hwee-Hwa

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.

Published in:

Mobile Data Management, 2006. MDM 2006. 7th International Conference on

Date of Conference:

10-12 May 2006