A Hybrid Content-Based and Collaborative Filtering Approach for Recommending Optimal Movie Release Months
DOI:
https://doi.org/10.30871/jaic.v10i2.11717Keywords:
Collaborative Filtering, Content based filtering, Film industry, Hybrid recommender system, Release strategyAbstract
The film industry faces increasingly intense competition, making release timing a critical factor in maximizing box-office performance. However, decisions regarding release months are often driven by producers’ intuition rather than systematic data analysis. This study proposes a data-driven decision-support system to recommend optimal movie release months by learning historical release and profitability patterns from previously released films. A hybrid recommender system combining Content-Based Filtering (CBF) and Collaborative Filtering (CF) is developed. The study utilizes data from The Movie Database (TMDB), including film genres, release dates, and profit margins. CBF measures content similarity based on genre features, while CF captures latent relationships between films and release months using historical profit patterns through Singular Value Decomposition (SVD). These two approaches are integrated using a weighted sum mechanism to produce a hybrid score. Model performance is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Experimental results demonstrate that the proposed hybrid model outperforms single-method approaches, achieving an RMSE of 1.60 and an MAE of 1.03, which are lower than those obtained by standalone CBF and CF models. Further analysis reveals that genres such as Action, Adventure, and Science-Fiction exhibit relatively stable profitability trends across specific release months. These findings indicate that the hybrid CBF–CF approach effectively captures both content-based similarity and temporal profitability patterns, making it suitable as a strategic decision-support tool for determining movie release timing.
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Copyright (c) 2026 Ukasyah Muntaha, Johannes Krisjon Silitonga, Elisabeth Claudia Simanjuntak, M. Syamsuddin Wisnubroto, Fajri Farid, Meida Cahyo Untoro
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