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A recommendation system based-on interactive evolutionary computation with data grouping
- A recommendation system based-on interactive evolutionary computation with data grouping
- Kim, H.-T.[Kim, Hyun Tae]; Ahn, C.W.[Ahn, Chang Wook]; An, J.[An, Jin Ung ]
- DGIST Authors
- An, J.[An, Jin Ung ]
- Issue Date
- Journal of Institute of Control, Robotics and Systems, 17(8), 739-746
- Article Type
- Calculations; Content-Based Filtering; Content Based Retrieval; Data Grouping; Electronic Commerce; Experimental Studies; Interactive Evolutionary Computation; Recommender System; Recommender Systems; Time Efficiencies
- Recently, recommender systems have been widely applied in E-commerce websites to help their customers find the items what they want. A recommender system should be able to provide users with useful information regarding their interests. The ability to immediately respond to the changes in user's preference is a valuable asset of recommender systems. This paper proposes a novel recommender system which aims to effectively adapt and respond to the immediate changes in user's preference. The proposed system combines IEC (Interactive Evolutionary Computation) with a content-based filtering method and also employs data grouping in order to improve time efficiency. Experiments show that the proposed system makes acceptable recommendations while ensuring quality and speed. From a comparative experimental study with an existing recommender system which uses the content-based filtering, it is revealed that the proposed system produces more reliable recommendations and adaptively responds to the changes in any given condition. It denotes that the proposed approach can be an alternative to resolve limitations (e.g., over-specialization and sparse problems) of the existing methods.
- Institute of Control, Robotics and Systems
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