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Interactive Genetic Algorithm Joining Recommender System

  • Po-Kai Wang
  • Chao-Fu HongEmail author
  • Min-Huei Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)

Abstract

The recommender systems tend to face the problem of lacking clues about the new user, as a result, it is difficult for the system to provide users with the right recommendation results. In this study, the Interactive genetic algorithm is adapted to join with recommender system, and this new system is used to solve the problem of the traditional recommender system. In addition, this study proposes a double-layer encoding structure that involves the global and area encoding, which will help the optimization of interactive genetic algorithm (IGA) to solve the user fatigue problem. The case study testified that this recommended framework was able to rapidly filter vast amounts of data and to offer personalized recommendations for each film viewer. For future research, further studies on IGA that integrates analysis of screen genes to achieve a higher quality of recommendation and to further solve IGA user fatigue issues. Finally, this new recommender system was used to test film viewers in Taiwan and Hollywood films released in the past 12 years, and the experimental results show evidence that the new system is useful.

Keywords

Recommender system Interactive Genetic Algorithms Hybrid recommendation Genre films 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Electrical Engineering and Computer ScienceNational United UniversityMiaoliTaiwan
  2. 2.Department of Information ManagementAletheia UniversityTaipeiTaiwan

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