Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Towards Creative Evolutionary Systems with Interactive Genetic Algorithm
Applied Intelligence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evolution Strategies with Subjective Selection
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Hybrid crossover operators for real-coded genetic algorithms: an experimental study
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Interactive evolution for cochlear implants fitting
Genetic Programming and Evolvable Machines
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Distributed probabilistic model-building genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Content-based recommendation systems
The adaptive web
Recommender Systems Handbook
Interactive genetic algorithms with individual's fuzzy fitness
Computers in Human Behavior
Interactive Evolutionary Computation-Based Hearing Aid Fitting
IEEE Transactions on Evolutionary Computation
Interactive genetic algorithms with large population and semi-supervised learning
Applied Soft Computing
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We apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point based on a user's Kansei through the interaction between the user and machine. However, especially in the domain of product recommendations, theremay be numerous optimum points. Therefore, the purpose of this study is to develop a new iGA crossover method that concurrently searches for multiple optimum points for multiple user preferences. The proposed method estimates the locations of the optimum area by a clustering method and then searches for the maximum values of the area by a probabilistic model. To confirm the effectiveness of this method, two experiments were performed. In the first experiment, a pseudouser operated an experiment system that implemented the proposed and conventional methods and the solutions obtained were evaluated using a set of pseudomultiple preferences. With this experiment, we proved that when there aremultiple preferences, the proposed method searches faster and more diversely than the conventional one. The second experiment was a subjective experiment. This experiment showed that the proposed method was able to search concurrently for more preferences when subjects had multiple preferences.