IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on learning with Bayesian networks
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Computer
COMPSAC '00 24th International Computer Software and Applications Conference
A Hybrid Approach to Discover Bayesian Networks From Databases Using Evolutionary Programming
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Incremental personalized web page mining utilizing self-organizing HCMAC neural network
Web Intelligence and Agent Systems
Web Intelligence and Agent Systems
Two-phase Web site classification based on Hidden Markov Tree models
Web Intelligence and Agent Systems
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Acquisition of a concession strategy in multi-issue negotiation
Web Intelligence and Agent Systems
Multiple criteria programming models for VIP E-Mail behavior analysis
Web Intelligence and Agent Systems
Qualitative preference-based service selection for multiple agents
Web Intelligence and Agent Systems
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Web Intelligence (WI) is a new and active research field in current AI and IT. Intelligent B2C Portals are an important research topic in WI. In this paper, we first investigate and analyze the architecture of a B2C portal for personalized recommendation from the viewpoint of conceptual levels of WI. Aiming at knowledge-level data mining in a B2C portal, we present a new improved learning algorithm of Bayesian Networks, which consists of two major contributions, namely, reducing Conditional Independence (CI) test costs by few lower order CI tests and accelerating search process by means of sort order for candidate parent nodes. Experimental results on benchmark ALARM data sets show that the improved algorithm has high accuracy, and is more efficient in the time performance than other algorithms. Finally, we apply this algorithm to learning Customer Shopping Model (CSM) in an intelligent recommendation system. By a number of experiments on real world data, we find that the recommendation method based on the learned CSM outperforms some traditional ones in rates of coverage and precision.