Belkacem Chikhaoui
ABSTRACT
This paper presents a practical and novel model for behavioral user profile construction using causal relationships. Causal relationships are extracted from behavior sequences for building user profiles. Our model discovers significant patterns from behavior sequences, then it discovers patterns associations using normalized mutual information. Causal relationships between significant patterns are then identified using the transfer entropy approach. We empirically demonstrate that these causality-based profiles accurately describe users profiles and allow developing practical Ubicomp applications.
- S. E. Middleton, N. R. Shadbolt, and D. C. De Roure. Ontological user profiling in recommender systems. ACM Trans. Inf. Syst., 22:54--88, 2004. Google Scholar
Digital Library
- J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000. Google ScholarDigital Library
- T. Schreiber. Measuring information transfer. Phys. Rev. Lett., 85:461--464, Jul 2000.Google ScholarCross Ref
- T. Xiong, S. Wang, Q. Jiang, and J. Z. Huang. A new markov model for clustering categorical sequences. In Proceedings of ICDM, pages 854--863, 2011. Google ScholarDigital Library
Index Terms
- Towards causal models for building behavioral user profile in ubiquitous computing applications
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