Movie Recommendation System Using Knowledge-Based Filtering and K-Means Clustering

  • Kurnia Drajat Wibowo Telkom University, Bandung, Indonesia
  • Z K A Baizal * Mail Telkom University, Bandung, Indonesia
  • (*) Corresponding Author
Keywords: Apriori; K-Means; Association Rule; Recommender System; Cold Start Problem


The movie recommender system has an important role in providing movie recommendations for users, but new users have difficulty choosing movies that are given by the recommender system because of the cold start problem. This study aims to overcome the cold start problem using a knowledge-based recommender system, i.e association rule mining using an apriori algorithm. The apriori algorithm aims to extract correlations between product itemsets, but the problem in the apriori algorithm is the large number of association rules that make the complex computation. To overcome this problem, we combine the apriori algorithm and k-means to produce more accurate recommendations, because the items are grouped before the recommendation process using the k-means algorithm. In this study, we use a dataset of movies and ratings from the Kaggle website. This study uses a minimum value of 0.5 confidence, and a minimum value of 4 lifts. To produce the best itemset in the form of antecedents and consequents of the Beauty and the Beast item with The Passion of Joan of Arc which has a value of 0.107981 support, 0.779661 confidence, 4.151695 lift


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Article History
Submitted: 2022-01-26
Published: 2022-03-31
Abstract View: 552 times
PDF Download: 366 times
How to Cite
Wibowo, K., & Baizal, Z. K. A. (2022). Movie Recommendation System Using Knowledge-Based Filtering and K-Means Clustering. Building of Informatics, Technology and Science (BITS), 3(4), 460-465.