MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Feature Selection Using Association Word Mining for Classification
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Enhancing digital libraries with TechLens+
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Knowledge and Information Systems
Context-based recommendation service in ubiquitous commerce
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
A user-item relevance model for log-based collaborative filtering
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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Nowadays, users spend much time and effort in finding the best suitable designs since more and more information is placed on-line. To save their time and effort in searching the designs they want, the user-adapting recommendation system is required. In this paper, Automatic Classification for Grouping designs (ACG) in a Fashion Design Recommendation Agent System (FDRAS) is proposed. The ACG algorithm groups designs into clusters based on these classified designs. It is possible that if the design requires simultaneous regrouping in all other groups, the ACG algorithm can be used to improve efficiency of information retrieval and sorting, in the FDRAS datasets. The proposed method is evaluated on a large database, significantly outperforming the nearest-neighbor model and k-mean clustering in the prototype user-adapting FDRAS. This method can solve the large-scale dataset problem without deteriorating accuracy quality.