2023 Proceedings

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Browsing 2023 Proceedings by Author "Akingbehin, V.C"

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    Open Access
    A Comparative Study of LDA and PCA Feature Extraction Techniques for Content Based Movie Recommendation Systems
    (MIRG, 2024-10-28) Abdul, U.S.; Akingbehin, V.C; Oladejo, T.O; Fasina, E.P; Odumuyiwa, V.
    With the rapid growth of digital streaming platforms and the ever-expanding catalogue of movies, effective movie recommendation systems have become crucial to enhance user engagement and satisfaction. Content-based recommendation models offer a personalized approach by leveraging intrinsic movie features to suggest relevant films to users based on their preferences. This paper compares two content-based movie recommendation models that utilizes movie attributes to generate tailored movie suggestions. The proposed models based on Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) start by extracting and encoding various content-related features from movies, such as genre, director, actors, plot keywords, and textual summaries. These features form a high-dimensional representation of each movie, capturing its unique characteristics. A user's preferences and viewing history are then used to build a user profile, representing their movie tastes and preferences. By comparing the user profile to the encoded movie representations, the models can identify movies that match users’ preferences. This research contributes to the advancement of content-based movie recommendation models by showcasing the effectiveness of selected movie features in providing personalized and relevant movie suggestions to users. It also demonstrates that the LDA model outperforms the PCA model on the MovieLens dataset.