Learning the users' preferences in e-commerce: A weight-adjustment approach
International Journal of Knowledge-based and Intelligent Engineering Systems - Soft Computing and its Applications to E-Business
Proceedings of the 2008 ACM conference on Recommender systems
MiSPOT: dynamic product placement for digital TV through MPEG-4 processing and semantic reasoning
Knowledge and Information Systems
A knowledge-based product recommendation system for e-commerce
International Journal of Intelligent Information and Database Systems
Intelligent tools for managing legal choices
Proceedings of the 13th International Conference on Artificial Intelligence and Law
HYREC: a hybrid recommendation system for e-commerce
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Minimally complete recommendations
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Hi-index | 0.00 |
This paper describes LCW, a procedure for learning customer preferences represented as feature weights by observing customers’ selections from return sets. An empirical evaluation on simulated customer behavior indicated that uninformed hypotheses about customer weights lead to low ranking accuracy unless customers place some importance on almost all features or the total number of features is quite small. In contrast, LCW’s estimate of the mean preferences of a customer population improved as the number of customers increased, even for larger numbers of features of widely differing importance. This improvement in the estimate of mean customer preferences led to improved prediction of individual customers’ rankings, irrespective of the extent of variation among customers and whether a single or multiple retrievals were permitted. The experimental results suggest that the return set that optimizes benefit may be smaller for customer populations with little variation than for customer populations with wide variation.