Causality-Based Model for User Profile Construction from Behavior Sequences

This paper presents a novel model for user profile construction using causal relationships. Causal relationships are extracted from behavior sequences to build user profiles. Our model first discovers significant patterns by adapting a new sequence clustering algorithm, and then discovers pattern as...

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Bibliographic Details
Main Authors: Chikhaoui, B., Shengrui Wang, Pigot, H.
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
behavior sequences
Causal relationships
Clustering algorithms
Computational modeling
Data mining
Entropy
pattern mining
Probabilistic logic
Smart homes
transfer entropy
user behavior
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Summary:This paper presents a novel model for user profile construction using causal relationships. Causal relationships are extracted from behavior sequences to build user profiles. Our model first discovers significant patterns by adapting a new sequence clustering algorithm, and then discovers pattern associations using normalized mutual information (NMI). Causal relationships between significant patterns are then extracted using the transfer entropy approach. These relationships are used to construct causal graphs of activities, to generate the user profile. In extensive experiments on a variety of datasets, we empirically demonstrate that these causality-based profiles yield a significant increase in performance on activity prediction.
ISSN:1550-445X
2332-5658
DOI:10.1109/AINA.2013.109