Optimizing selection using Pearson correlation coefficient: A case study in movie industry

Recommendation systems are widely used as a reference in marketing products or businesses. The number of choices given sometimes makes a person confused in making choices. The recommendation system provides a solution in overcoming this. Collaborative filtering is a fairly popular algorithm used in recommendation systems that are based on references and information obtained from users. User rating is a variable that is widely used as a reference in establishing a recommendation system. User rating can influence other users in making their choice of the same product. The purpose of this study is to conduct an experiment in the application of a collaborative filtering algorithm and use the Pearson correlation function as a method used to see the level of user similarity in making a restaurant recommendation system. The methods used in this research are: (1) datasets preparation, (2) datasets filtering, (3) collaborative filtering, (4) pearson correlation, (5) recommendations, and (6) evaluation. Collaborative filtering combined with the Pearson correlation produces recommendations that are suitable for users based on characteristics or the same level of interest between users, making it easier for users to make choices. This is also indicated by a small error rate based on the RMSE results.

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Optimizing selection using Pearson correlation coefficient: A case study in movie industry

Cited by 2 publications

References 4 publications

A critical review of the correlative effect of process parameters on pyrolysis of plastic wastes

A critical review of the correlative effect of process parameters on pyrolysis of plastic wastes

Restaurant Recommendation System Based on User Ratings with Collaborative Filtering

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