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A New Evolutionary Approach to Recommender Systems
- A New Evolutionary Approach to Recommender Systems
- Kim, HT[Kim, Hyun-Tae]; An, JN[An, Jinung]; Ahn, CW[Ahn, Chang Wook]
- DGIST Authors
- An, JN[An, Jinung]
- Issue Date
- IEICE Transactions on Information and Systems, E97D(3), 622-625
- Article Type
- Content-Based Filtering; Data Grouping; Evolutionary Approach; Information Science; Interactive Evolutionary Computation; Recommendation Methods; Recommender Systems; Software Engineering; User' s Preference; User' s Preferences
- In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods. Copyright © 2014 The Institute of Electronics, Information and Communication Engineers.
- IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
- Related Researcher
Brain Robot Interaction Lab
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