Citation :

Nisha Bhalse, Ramesh Thakur, Archana Thakur, "Novel Movie Recommendation System using K-Means and Mean Shift Clustering," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 3, pp. 248-257, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I3P118

Abstract

Online shopping has risen during the COVID-19 pandemic. Nowadays, recommendation systems are important for providing personalized suggestions. Recommendation Systems (RS) face the challenges of efficiently and relevantly providing suggestions from the large volume of information. Many fields use recommendation systems, such as movies, e-commerce, and news. A Collaborative Filtering (CF) algorithm is an effective RS technique that recommends items that are similar to the active user's items. CF is caused by data sparsity, cold start, and scalability problems. The proposed Novel Hybrid K-means and Mean-Shift Clustering (NHMM) algorithm for recommending movies based on user preferences. Based on users’ past preferences, the input is collected from the MovieLens 1M dataset. The NHMM model is preprocessed and trained on the MovieLens dataset, and it recommends the top-k movies to the user based on the user’s interest preferences. The proposed NHMM model performance was evaluated with different recommendation techniques: k-means clustering, collaborative filtering, and matrix factorization. The experiment shows that the comprehensive results of the proposed NHMM model achieve the highest accuracy of 92.4%, precision 93.3%, recall 90.8%, and F1-score 91.9%. The proposed NHMM model recommends accurate, relevant top-k movies to users. The proposed NHMM model achieves the lowest RMSE (0.798) and MAE (0.633). The results show that the proposed NHMM model recommends accurate and robust, as well as diverse and serendipitous, top-k movies to users compared to other traditional models.

Keywords

Recommendation System, Collaborative Filtering, K-means Clustering, Mean Shift Clustering.

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