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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
BellogÃn, A.: Recommender System Performance Evaluation and Prediction: An Information Retrieval Perspective (2012). https://goo.gl/CACxqn
Bäck, T., Schütz, M.: Intelligent mutation rate control in canonical genetic algorithms. In: Raś, Zbigniew W., Michalewicz, M. (eds.) ISMIS 1996. LNCS, vol. 1079, pp. 158–167. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61286-6_141
Badrul, S., George, K., Joseph, K., John, R.: Item-Based Collaborative Filtering Recommendation Algorithms (2001). https://goo.gl/TFkqwD
Marko, B., Yoav, S.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Fred, D.: User acceptance of information technology: system characteristics, user perceptions, and behavioral impacts. Int. J. Man-Mach. Stud. 38(3), 475–487 (1993)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor (1975)
Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 247–254. ACM (2001)
Li-Chieh, H.: A study of customer value-orientation and negotiation. Master’s thesis, Southern Taiwan University of Science and Technology, Tainan City (2005). https://goo.gl/oFvpKJ
Vorderer, P., Klimmt, C., Ritterfeld, U.: Enjoyment: at the heart of media entertainment. Commun. Theor. 14(4), 388–408 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, PK., Hong, CF., Lin, MH. (2019). Interactive Genetic Algorithm Joining Recommender System. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-14802-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14801-0
Online ISBN: 978-3-030-14802-7
eBook Packages: Computer ScienceComputer Science (R0)