GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
A randomized linear-time algorithm to find minimum spanning trees
Journal of the ACM (JACM)
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
“Is this document relevant?…probably”: a survey of probabilistic models in information retrieval
ACM Computing Surveys (CSUR)
Information Retrieval
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Building Solutions with Microsoft Commerce Server 2002
Building Solutions with Microsoft Commerce Server 2002
Recommender systems using linear classifiers
The Journal of Machine Learning Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Adaptive attribute selection for configurator design via shapley value
Artificial Intelligence for Engineering Design, Analysis and Manufacturing - Configuration
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
Design by customer: concept and applications
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing
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Product customization is attracting more attentions in industry as a viable strategy to better meet customer requirements and gain more profit. However the vast number of product variants in product customization process often makes it difficult for consumers to make purchase decisions, a phenomenon referred to as information overload. In this paper we take a two-prong approach to tackle the issue of information overload in customized products recommendation. Basically, the method answers two questions, namely, which products to recommend and in what order to present the recommendations. Firstly, a probability relevance model is deployed to calculate the probability of relevance for each end product. Then a probability ranking principle is exploited to present the recommendations. The approach also takes customer flexibility into consideration and thus mitigates the effect of inconsistent specifications from customers. It does not require any prior knowledge about an active customer's preference and can accommodate the new customers challenge facing by recommendation approaches. Analytical results show that the method is optimal in terms of customer's utility and product recommendation efficiency. Numerical experiments are also conducted to test the presented approach.