Memory-based Collaborative Filtering on Twitter Using
Support Vector Machine Classification

Memory-based Collaborative Filtering on Twitter Using
Support Vector Machine Classification

Recommender System, User-based, Item-based, Collaborative Filtering, Support Vector Machine

Nowadays, watching films at home is one of people's entertainment. Netflix is a service provider for watching films and provides
many types of film genres. However, of the many films available, it makes users confused to choose which film to watch first.
The solution to the problem is a system that provides recommendations for the best films to watch based on user ratings. Twitter
is still people's favorite social media to express their feelings, thoughts, and criticisms. In this system, tweets serve as input
data that will be processed into data with rating values. This research implemented a recommendation system based on user
ratings from tweets using collaborative filtering combined with Support Vector Machine (SVM) classification and implemented
it on user-based and item-based. The test results in this study show that Collaborative Filtering gets the best RMSE value
results on item-based 0.5911 and 0.8162 on user-based. The Support Vector Machine (SVM) classification algorithm using
hyperparameter tuning produces item-based values with a precision of 85.03% and recall of 90.71%, while user-based values
with a precision of 87.75% and recall of 88.95%

Anang Furkon Rifai1
, Erwin Budi Setiawan2

Telkom University

: 01-10-2022

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Indonesia

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