Fab: content-based, collaborative recommendation
Communications of the ACM
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Studying Recommendation Algorithms by Graph Analysis
Journal of Intelligent Information Systems
A graph model for E-commerce recommender systems
Journal of the American Society for Information Science and Technology
Learning mixtures of DAG models
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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An Integrated-Graph model for user interests in personalized recommendation, which is based on Small-World network and Bayesian network, is presented The Integrated-Graph model consists of two layers. One is user's layer for representing users or customers and the other is merchandise's layer for representing goods or produce. The relationships among users are described by Small-World network at lower layer. The implications among merchandises are represented by Bayesian network at higher layer. Directed arcs denote the interests and tendency between user's layer and merchandise's layer. Several algorithms for clustering and interest analysis based on Small-World network are introduced The experimentation shows that the model can well represent the relationships among users to users, merchandise to merchandise, and users to merchandise. The result of interest recommendation based this Integrated-Graph mode is better than others.