Discovering Causal Dependencies in Mobile Context-Aware Recommenders

• Main Authors: YAP, Ghim-Eng, TAN, Ah-Hwee, PANG, Hwee Hwa
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: Context acquisition; Context model; Restaurant recommender system; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/526; https://ink.library.smu.edu.sg/context/sis_research/article/1525/viewcontent/CausalDependencies_MobileContext_awareRecommenders_2006.pdf
• Institution: Singapore Management University

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.