Topic aspects-based generative mixture model for movie recommendation system using deep convolutional network

Maha Al-Ghalibi; Kai Lawonn

doi:10.1117/12.2556294

31 January 2020 Topic aspects-based generative mixture model for movie recommendation system using deep convolutional network

Maha Al-Ghalibi, Kai Lawonn

Author Affiliations +

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114333K (2020) https://doi.org/10.1117/12.2556294
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Movie recommendation systems have become ubiquitous in most sides of our lives. Currently, they are far from optimal. This paper presents a movielense recommendation system based on machine learning through utilizing the deep convolutional network and depending on generative modeling of public previous aspects mixtures. The objective of this paper is to introduce such a recommendation system to help users in selecting datasets of movies according to certain pre-specified measurements and data. The applied methodology is pivoted on implementing the system by using different sentimental analysis algorithms. These algorithms are keen to provide a solution for the full stack developers through using a trained model using their datasets. This will give suggestions based on their previous activity or recommended by other users’ interests demonstrated on their website. Thus to help users visualize their interest or to form the better scope of visualization. The presented system has proved better results concerning accuracy and efficiency in comparison with some other similar works. When experimentations on both real and synthetic datasets were conducted, the system showed percentile improvement of about 91.07%in the training dataset and 93.49%in the testing dataset respectively. This system is convenient for several application fields like time series network visualization, business process modeling, various data mining applications, e-commerce websites, besides most online platforms that people use including social media.

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Maha Al-Ghalibi and Kai Lawonn "Topic aspects-based generative mixture model for movie recommendation system using deep convolutional network", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114333K (31 January 2020); https://doi.org/10.1117/12.2556294

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KEYWORDS

Data modeling

Machine learning

Statistical modeling

Systems modeling

Convolutional neural networks

Evolutionary algorithms

Visualization

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