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
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
Online Recommendation Based on Customer Shopping Model in E-Commerce
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders
IEEE Transactions on Knowledge and Data Engineering
Knowledge discovery for adaptive negotiation agents in e-marketplaces
Decision Support Systems
A collaborative recommender system based on probabilistic inference from fuzzy observations
Fuzzy Sets and Systems
Web Intelligence and Agent Systems
A Probabilistic Approach for Mining Drifting User Interest
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Bayesian network modeling for evolutionary genetic structures
Computers & Mathematics with Applications
Bayesian networks learning for strategies in artificial life
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
Web Intelligence (WI) is a new and active research field in current AI and IT. Personalized recommendation in an intelligent B2C portal is an important research topic in WI. In this paper, we first investigate the architecture of a B2C portal from the viewpoint of conceptual levels of WI. Aiming at data mining of knowledge-level in a B2C portal, we present a new improved learning algorithm of Bayesian Networks, which consists of two major contributions, namely, making the best of lower order Conditional Independence (CI) tests and accelerating search process by means of sort order for parent nodes. By a number of experiments on ALARM datasets, we find that the proposed algorithm is both more efficient and effective than others. We have applied this algorithm to a commodity recommendation system in a B2C portal. Our experimental results demonstrate that the recommendation method based on a Customer Shopping Model (CSM) produced by the new algorithm outperforms some traditional ones in rates of coverage and precision.