Web-Based Movie Recommendation System using Content-Based Filtering and KNN Algorithm

Delimayanti, Mera Kartika; Laya, Mauldy; Warsuta, Bambang; Faydhurrahman, Mas Brillianesa; Khairuddin, Mohammad Azri; Ghoyati, Hubbaka; Mardiyono, Anggi; Naryanto, Rizqi Fitri

doi:10.1109/icitacee55701.2022.9923974

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Term Frequency -Inverse Document Frequency (TF-IDF) is one of the weighting techniques that performs calculation by multiplying the frequency of occurrence of words in documents (TF), with a value (IDF) which is the logarithm of the inverse proportion of documents in coripus that contain the word [15] [18].…”

Section: Tf-idfmentioning

confidence: 99%

Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD

Salsabil,

Setiawan,

Kurniawan

2024

KINETIK

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The application of recommendation systems has been applied in various types of platforms, especially applications for watching movies such as Netflix and Disney+. The recommendation system is purposed to make it easier for users, especially in choosing a movie because currently the number of movie productions is increasing every day. This research proposed a CBF movie recommendation system by comparing the performance of several semantic methods to be able to get the best rating prediction results. In order to improve the performance quality to get the best rating prediction results, this research utilized semantic feature methods by comparing the performance of the evaluation results produced by the TF-IDF method and word embedding applications, such as BERT, GPT-2, RoBERTa, and implemented RNN model to classify the results of rating prediction. The data were used to generate the recommendation system by involving 854 data movie and 39 accounts with a total of 34,056 movie reviews on Twitter. This research has succeeded in getting a method that produced rating predictions, namely RoBERTa. In the classification process with the RNN model and SGD optimization, the measurement results with confusion matrix by classifying the RoBERTA rating prediction obtained an evaluation value of 0.6514 loss, 95.59% accuracy, and 0.6514 precision.

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Section: Tf-idfmentioning

confidence: 99%