Analisis Perbandingan Algoritma TF-IDF dan Word2Vec dalam Rekomendasi Film Berbasis Konten Letterboxd
Systematic Literature Review
DOI:
https://doi.org/10.55606/jutiti.v6i2.7244Keywords:
Content-Based Filtering, Letterboxd, Natural Language Processing, Recomendation Film System, TF-IDF, Word2VecAbstract
The exponential growth of film streaming platforms has created significant challenges for users in discovering content that aligns with their preferences. Recommendation systems have proven to be a strategic solution. This study aims to compare the performance of Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec algorithms in the context of content-based filtering recommendation systems, utilizing data from the Letterboxd platform. The research was conducted through a Systematic Literature Review (SLR) of 15 prior studies, with a selection process comprising the definition of inclusion and exclusion criteria, literature searches across academic databases, and comparative synthesis of findings. The review findings indicate that TF-IDF excels in weighting explicit terms within structured metadata such as genre, director, and cast, and can be implemented without a complex training process. Word2Vec, on the other hand, demonstrates superiority in capturing semantic relationships between words through dense vector representations, yet requires a large corpus and careful hyperparameter tuning. Based on the literature synthesis, TF-IDF combined with Cosine Similarity is deemed more optimal for a content-based film recommendation system using Letterboxd metadata, given the structured and explicit nature of the data. Recall@5 of 73% and Recall@10 of 80% reported in prior studies further support this conclusion
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