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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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2006
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| 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. |
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