Modeling acoustic correlations by factor analysis
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Mixtures of Local Linear Subspaces for Face Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Identifying Users Stereotypes with Semantic Web Mining
ER '08 Proceedings of the ER 2008 Workshops (CMLSA, ECDM, FP-UML, M2AS, RIGiM, SeCoGIS, WISM) on Advances in Conceptual Modeling: Challenges and Opportunities
Topic-based user segmentation for online advertising with latent dirichlet allocation
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
A habit mining approach for discovering similar mobile users
Proceedings of the 21st international conference on World Wide Web
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This paper proposes an approach for Web user segmentation and online behavior analysis based on a mixture of factor analyzers (MFA). In our proposed framework, we model users’ shared interests as a set of common latent factors extracted through factor analysis, and we discover user segments based on the posterior component distribution of a finite mixture model. This allows us to measure the relationships between users’ unobserved conceptual interests and their observed navigational behavior in a principled probabilistic manner. Our experimental results show that the MFA-based approach results in finer-grained representation of user behavior and can successfully discover heterogeneous user segments and characterize these segments with respect to their common preferences.