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Hybrid Neural Genetic Architecture: New Directions for Intelligent Recommender System Design

Hybrid Neural Genetic Architecture: New Directions for Intelligent Recommender System Design

Emmanuel Buabin
Copyright: © 2013 |Pages: 26
ISBN13: 9781466625426|ISBN10: 1466625422|EISBN13: 9781466625433
DOI: 10.4018/978-1-4666-2542-6.ch013
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MLA

Buabin, Emmanuel. "Hybrid Neural Genetic Architecture: New Directions for Intelligent Recommender System Design." Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods, edited by Satchidananda Dehuri, et al., IGI Global, 2013, pp. 245-270. https://doi.org/10.4018/978-1-4666-2542-6.ch013

APA

Buabin, E. (2013). Hybrid Neural Genetic Architecture: New Directions for Intelligent Recommender System Design. In S. Dehuri, M. Patra, B. Misra, & A. Jagadev (Eds.), Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods (pp. 245-270). IGI Global. https://doi.org/10.4018/978-1-4666-2542-6.ch013

Chicago

Buabin, Emmanuel. "Hybrid Neural Genetic Architecture: New Directions for Intelligent Recommender System Design." In Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods, edited by Satchidananda Dehuri, et al., 245-270. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2542-6.ch013

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Abstract

The objective is a neural-based feature selection in intelligent recommender systems. In particular, a hybrid neural genetic architecture is modeled based on human nature, interactions, and behaviour. The main contribution of this chapter is the development of a novel genetic algorithm based on human nature, interactions, and behaviour. The novel genetic algorithm termed “Buabin Algorithm” is fully integrated with a hybrid neural classifier to form a Hybrid Neural Genetic Architecture. The research presents GA in a more attractive manner and opens up the various departments of a GA for active research. Although no scientific experiment is conducted to compare network performance with standard approaches, engaged techniques reveal drastic reductions in genetic operator operations. For illustration purposes, the UCI Molecular Biology (Splice Junction) dataset is used. Overall, “Buabin Algorithm” seeks to integrate human related interactions into genetic algorithms as imitate human genetics in recommender systems design and understand underlying datasets explicitly.

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