Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Personalized recommendation on dynamic content using predictive bilinear models
Proceedings of the 18th international conference on World wide web
Efficient real time maintenance of retrieval knowledge in case-based reasoning
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
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Though the research in personalized recommendation systems has become widespread for recent years, IEEE Internet Computing points out that current system can not meet the real large-scale e-commerce demands, and has some weakness such as low precision and slow reaction. We have proposed a structure of personalized recommendation system based on case intelligence, which originates from human experience learning, and can facilitate to integrate various artificial intelligence components. Addressing on user case retrieval problem, the paper uses constructive and understandable multi-layer feed-forward neural networks (MFNN), and employs covering algorithm to decrease the complexity of ANN algorithm. Testing from the two different domains, our experimental results indicate that the integrated method is feasible for the processing of vast and high dimensional data, and can improve the recommendation quality and support the users effectively. The paper finally signifies that the better performance mainly comes from the reliable constructing MFNN.