Implementation of Two-Stage Collaborative Filtering Method with Diversity Balancing for Movie Recommendation System
DOI:
https://doi.org/10.30871/jaic.v10i2.12293Keywords:
Collaborative Filtering, Diversity Balancing, K-Means Clustering, Recommender System, Two-Stage MethodAbstract
In the digital era, users often face difficulties in selecting products or content due to the overwhelming amount of available information. While recommender systems, particularly Collaborative Filtering (CF), help address this issue, they often suffer from a crucial weakness: a tendency to recommend popular and homogeneous items. This focus on accuracy leads to "less diverse" recommendations, trapping users in monotonous choices. To address this problem, this study contributes by integrating K-Means-based clustering with a Two-Stage Collaborative Filtering approach and a diversity balancing re-ranking mechanism to mitigate recommendation over-specialization while maintaining predictive accuracy. The process begins with K-Means Clustering to handle data sparsity and improve efficiency, followed by candidate generation using Item-Based CF, and finally, a re-ranking process to balance accuracy and diversity. Experimental results using the IMDb dataset demonstrate that the proposed method successfully provides diverse recommendations across various genres, such as Action, Drama, and Romance, without sacrificing relevance. The system achieves a Mean Absolute Error (MAE) of 0.8214 in modern movie scenarios, indicating that the integration of diversity balancing maintains robust predictive accuracy while significantly enhancing the variety of recommended items.
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