Abstract
Recently, many solutions and sites related to the intelligent agent are created in order to provide good services for customers. Moreover, some new proposals including the collaborative filtering are put forward in the field of electronic commerce (EC) solutions. However, these proposals are lack of the add-on characteristics. In fact, it seems that only a few intelligent systems could provide the recommendations to the customers for the items that they really want to purchase, by means of the collaborative filtering algorithm based on their previous evaluation data. In this paper, we propose the CLASG (Clustering And Similarity Grouplens) collaborative filtering agent algorithm. The CLASG algorithm is the one that uses both the GroupLens algorithm and the clustering method. We have evaluated its performance with enough experiments, and the results show that the proposed method provides more stable recommendations than GroupLens does. We developed the MindReader, which makes it possible to have the correct predictions and recommendations with less response time than ever, as an automated recommendation system that includes both of CLASG algorithm and WhoLiked agent. It can be readily integrated into the existing EC solutions since it has an add-on characteristic, which is lacked in the past solutions.
Keywords
- Recommender System
- Current Node
- Collaborative Filter
- Average Error Rate
- MindReader System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2002 Springer-Verlag Berlin Heidelberg
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Hwang, B., Kim, E., Lee, B. (2002). An Efficient Intelligent Agent System for Automated Recommendation in Electronic Commerce. In: Hacid, MS., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds) Foundations of Intelligent Systems. ISMIS 2002. Lecture Notes in Computer Science(), vol 2366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48050-1_44
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DOI: https://doi.org/10.1007/3-540-48050-1_44
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