Utilization of Demographic Analysis with IMDB User Ratings on the Recommendation of Movies

Bae, Sung-Moon; Lee, Sang Chun; Park, Jong‐Hun

doi:10.7838/jsebs.2014.19.3.125

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Generally, a demographics filtering recommendation system will involve user demographic information such as gender, age, occupation, etc. to perform group clustering [8].…”

Section: Demographic Filteringmentioning

confidence: 99%

Adopting Machine Learning in Demographic Filtering for Movie Recommendation System

Yin¹,

Safar²,

Kamaludin³

et al. 2023

JSCDM

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In this era of big data explosion, humans widely use the movie recommendation system as an information tool. There are two common issues found in the machine learning movie recommendation system that is still undeniable: first, cold start, and second, data sparsity. To minimize the problems, a research study is conducted to find a decision-making algorithm to solve the complex start problem in a movie recommendation system with precise parameters. It involves the implementation of the proposed demographics filtering technique with the k-means clustering method. The research findings present the effects of demographic filtering for movie recommendations. Demographic filtering can group users into clusters based on gender, age group, and occupation. The clusters distribution representative group based on the top 100 results of the experiment. The user with the least distance to the cluster center is chosen as the usual group in that cluster. Three clusters were experimented: Cluster 0, Cluster 1, and Cluster 2. Cluster 0 has a representative group of male, college, or graduate students aged 25 to 34. Cluster 1 has a representative group of females, executive or managerial, aged 25 to 34. Cluster 2 has a representative group of males, sales or marketing aged 35 to 44. It is shown that user from different collection has various preferred movie genre. The preferred movie genre in Cluster 0 is action, adventure, comedy, drama, and war. Cluster 1 has preferred comedy, crime, drama, horror, romance, and sci-fi movie genres. Cluster 2 has chosen action, comedy, drama, film-noir, mystery, and thriller movie genres. This research has contributed to the demographic filtering studies as an alternative solution for future technical development work.

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“…Generally, a demographics filtering recommendation system will involve user demographic information such as gender, age, occupation, etc. to perform group clustering [8].…”

Section: Demographic Filteringmentioning

confidence: 99%

Adopting Machine Learning in Demographic Filtering for Movie Recommendation System

Yin¹,

Safar²,

Kamaludin³

et al. 2023

JSCDM

View full text Add to dashboard Cite

show abstract

“…In [3] the connection between user voting data and economic film characteristics such as budget and box office data has been investigated by Wasserman et al Hsu et at., using linear combination, multiple linear regression, neural networks predicted the IMDb rating from other movie's attributes using 32968 titles [4]; and In [5] Nithin et al used Logistic Regression, SVM Regression and Linear Regression to predict box office data. In [6], using available demographic information on IMDb Bae et al created a demographic movie recommender system. Ramos et al showed the distribution of votes showed a scale-free behavior [7].…”

Section: Introductionmentioning

confidence: 99%

IMDb data from Two Generations, from 1979 to 2019; Part one, Dataset Introduction and Preliminary Analysis

Bahraminasr,

Sadr

2020

Preprint

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IMDb.com" as a user-regulating and one the most-visited portal has created an opportunity to create an enormous database. Analysis of the information on Internet Movie Database -IMDb, either those related to the movie or provided by users, would help to reveal the determinative factors in the route of success for each movie. As the lack of a comprehensive dataset was felt, we determined to do create a compendious dataset for the later analysis using the statistical methods and machine learning models. The present paper is the first paper in a series of papers aiming the mentioned goals, by a brief description of the created dataset and a demo of data analysis.

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Quantitative approaches for evaluating the influence of films using the IMDb database

Canet

2016

Communication & Society

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Why do films certain remain influential throughout film history? The purpose of this paper is to attempt to answer this question. To do so, we adopt some quantitative approaches that facilitate an objective interpretation of the data. The data source we have chosen for this study is the Internet Online Movie Database (IMDb), and in particular, one of its sections called "Connections", which lists references made to a film in subsequent movies and references made in the film itself to previous ones. The extraction and analysis of these networks of citations allows us to draw some conclusions about the most influential movies in film history, identifying their distinguishing features, and considering how their popularity has evolved over time.

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Utilization of Demographic Analysis with IMDB User Ratings on the Recommendation of Movies

Cited by 4 publications

References 12 publications

Adopting Machine Learning in Demographic Filtering for Movie Recommendation System

Adopting Machine Learning in Demographic Filtering for Movie Recommendation System

IMDb data from Two Generations, from 1979 to 2019; Part one, Dataset Introduction and Preliminary Analysis

Quantitative approaches for evaluating the influence of films using the IMDb database

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