Movie Recommendation System
K. Raj, Atulya Abhinav Das, Antariksh Guha
Apr 30, 2019
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Journal
International Journal of Computer Sciences and Engineering
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Semantic Scholar
Key Takeaway Key Takeaway: The proposed personalized movie recommendation system using neural network classification and hybrid optimization algorithm provides more accurate ratings with less time, improving performance in the movie database.
Abstract
The objective of this work is to assess the utility of personalized recommendation system (PRS) in the field of movie recommendation using a new model based on neural network classification and hybrid optimization algorithm. We have used advantages of both the evolutionary optimization algorithms which are Particle swarm optimization (PSO) and Bacteria foraging optimization (BFO). In its implementation a NN classification model is used to obtain a movie recommendation which predict ratings of movie. Parameters or attributes on which movie ratings are dependent are supplied by user’s demographic details and movie content information. The efficiency and accuracy of proposed method is verified by multiple experiments based on the Movie Lens benchmark dataset. Hybrid optimization algorithm selects best attributes from total supplied attributes of recommendation system and gives more accurate rating with less time taken. In present scenario movie database is becoming larger so we need an optimized recommendation system for better performance in terms of time and accuracy.