Agents that reduce work and information overload
Communications of the ACM
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Communications of the ACM
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Evolving Behaviors for Cooperating Agents
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Problem Decomposition and Multi-agent System Creation for Distributed Problem Solving
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Empirical Study of Recommender Systems Using Linear Classifiers
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
RecTree: An Efficient Collaborative Filtering Method
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
| Hi-index | 0.00 |
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.