Probabilistic User Behavior Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Modeling user behavior in recommender systems based on maximum entropy
Proceedings of the 16th international conference on World Wide Web
Eliciting Motivation Knowledge from Log Files Towards Motivation Diagnosis for Adaptive Systems
UM '07 Proceedings of the 11th international conference on User Modeling
CONVEX: Similarity-Based Algorithms for Forecasting Group Behavior
IEEE Intelligent Systems
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
Mining Periodic Behavior in Dynamic Social Networks
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Learning and inferencing in user ontology for personalized Semantic Web search
Information Sciences: an International Journal
SOAR: An Extended Model-Based Reasoning for Diagnosing Faults in Service-Oriented Architecture
SERVICES '09 Proceedings of the 2009 Congress on Services - I
Improving the Behavior of Intelligent Tutoring Agents with Data Mining
IEEE Intelligent Systems
Scalable learning of collective behavior based on sparse social dimensions
Proceedings of the 18th ACM conference on Information and knowledge management
A User Behavior Perception Model Based on Markov Process
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
Modeling user interests by conceptual clustering
Information Systems
A creative abduction approach to scientific and knowledge discovery
Knowledge-Based Systems
A Nonsupervised Learning Framework of Human Behavior Patterns Based on Sequential Actions
IEEE Transactions on Knowledge and Data Engineering
A Dominance-based Rough Set Approach to customer behavior in the airline market
Information Sciences: an International Journal
Cosmetics purchasing behavior - An analysis using association reasoning neural networks
Expert Systems with Applications: An International Journal
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
Analysis on repeat-buying patterns
Knowledge-Based Systems
User Navigation Behavior Mining Using Multiple Data Domain Description
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Towards Understanding How Personality, Motivation, and Events Trigger Web User Activity
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Creating Evolving User Behavior Profiles Automatically
IEEE Transactions on Knowledge and Data Engineering
Hi-index | 12.05 |
Many studies have been proposed to research behavior mining. However, in many cases, the aim of exploring behaviors is to exploit their motivations. Based on discovered behavioral reasons, we are able to conduct subsequent actions to impel or impede those behaviors. Although some logical approaches have been proposed to derive an explanation for a set of observations using abductive reasoning, there are few methods that take a statistical approach for group behavioral reason mining. Statistical methods enable us to discover behavioral reasons automatically in an uncertain situation. To address this issue, we propose a computational model and a family of algorithms called BRMA (Behavioral Reason Mining Algorithm), which exploits various distance functions to discover group behavioral reasons in three statistical ways. The BRMA algorithms have low time complexity and run extremely fast. Based on two datasets, we conducted comprehensive experiments to evaluate the effectiveness of the BRMA algorithms. The empirical experimental results indicate that the BRMA algorithms have a relatively high accuracy, and that among the BRMA family, BRMA^M^P outperforms BRMA^A^v^e^r^a^g^e and BRMA^W^e^i^g^h^t.