Movie Rating Estimation Based on Weakly Supervised Multi-modal Latent Variable Model

Watanabe, Koshi; Maeda, Kazuo; Ogawa, Takahiro; Haseyama, Miki

doi:10.1109/gcce53005.2021.9621938

Cited by 1 publication

(1 citation statement)

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“…In Shafaei, Smailis, Kakadiaris and Solorio (2021), the authors classified the movies using the text, sound, and image of the movies, along with a deep neural network, into two categories: suitable and unsuitable for children. In Watanabe, Maeda, Ogawa and Haseyama (2021), a method for estimating the score (success) of the film presented based on a multi-dimensional latent variable model with poor supervision. Authors in Maragatham et al (2021) benefited a block chain system to accurately rate movies based on user feedback.…”

Section: A Review Of Research Literaturementioning

confidence: 99%

A Movie recommender system based on topic modeling using machine learning methods

Kordabadi

Nazari

Mansoorizadeh

2022

Preprint

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In recent years, we have seen an increase in the production of films in a variety of categories and genres. Many of these products contain concepts that are inappropriate for children and adolescents. Hence, parents are concerned that their children may be exposed to these products. As a result, a smart recommendation system that provides appropriate movies based on the user’s age range could be a useful tool for parents. Existing movie recommender systems use quantitative factors and metadata that lead to less attention being paid to the content of the movies. This research is motivated by the need to extract movie features using information retrieval methods in order to provide effective suggestions. The goal of this study is to propose a movie recommender system based on topic modeling and text-based age ratings. The proposed method uses latent Dirichlet allocation (LDA) modelling to identify hidden associations between words, document topics, and the levels of expression of each topic in each document. Machine learning models are then used to recommend age-appropriate movies. It has been demonstrated that the proposed method can determine the user’s age and recommend movies based on the user’s age with 93% accuracy, which is highly satisfactory.

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Section: A Review Of Research Literaturementioning

confidence: 99%