Movie Recommender System Using Decision Tree Method

Muhammad Bilal Rafif Azaki
Z. K. A. Baizal - [ ]



In this modern era, many things that can be done online, one of which is watching movies. When the number of movies increases, people often find it difficult to decide which movie to watch next. To solve this problem, a useful recommendation system was developed to find movies that one might like based on movies that have been watched before. This research develops a movie recommendation system using Collaborative Filtering (CF) with the Decision Tree algorithm. In this study, the data used were movie data and ratings obtained from the website. Then the movielens dataset is filtered and only saves movies with a rating of more than 50 that are used in the recommendation system. In this study, Mean Absolute Error (MAE) is used as a method to assess the accuracy of the movie recommendation system. Based on the research that has been done, Decision Tree gets better results with an MAE value of 0,942 compared to Collaborative Filtering with an MAE value of 1,242.


recommender system, movie recommender system, Decision tree, collaborative filtering

Full Text:


Article Metrics :


T. Sharma, R. DIchwalkar, S. Milkhe, and K. Gawande, “Movie buzz-movie success prediction system using machine learning mod-el,” in Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020, Dec. 2020, pp. 111–118. doi: 10.1109/ICISS49785.2020.9316087.

M. Gupta, A. Thakkar, Aashish, V. Gupta, and D. P. S. Rathore, “Movie Recommender System Using Collaborative Filtering,” in 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Jul. 2020, pp. 415–420. doi: 10.1109/ICESC48915.2020.9155879.

S. Reddy, S. Nalluri, S. Kunisetti, S. Ashok, and B. Venkatesh, “Content-based movie recommendation system using genre correla-tion,” in Smart Innovation, Systems and Technologies, 2019, vol. 105, pp. 391–397. doi: 10.1007/978-981-13-1927-3_42.

A. Pal, P. Parhi, and M. Aggarwal, “An improved content based collaborative filtering algorithm for movie recommendations,” in 2017 Tenth International Conference on Contemporary Computing (IC3), Aug. 2017, pp. 1–3. doi: 10.1109/IC3.2017.8284357.

U. Thakker, R. Patel, and M. Shah, “A comprehensive analysis on movie recommendation system employing collaborative filtering,” Multimed Tools Appl, vol. 80, no. 19, pp. 28647–28672, Aug. 2021, doi: 10.1007/s11042-021-10965-2.

M. Srifi, A. Oussous, A. A. Lahcen, and S. Mouline, “Recommender systems based on collaborative filtering using review texts-A survey,” Information (Switzerland), vol. 11, no. 6. MDPI AG, Jun. 01, 2020. doi: 10.3390/INFO11060317.

S. D. Jadhav and H. P. Channe, “Efficient Recommendation System Using Decision Tree Classifier and Collaborative Filtering,” Inter-national Research Journal of Engineering and Technology, 2016, [Online]. Available:

J. Zhang, Y. Wang, Z. Yuan, and Q. Jin, “Personalized Real-Time Movie Recommendation System: Practical Prototype and Evalua-tion,” 1007. [Online]. Available:

G. Srivastav, R. H. Singh, S. Maurya, T. Tripathi, and T. Narula, “Movie Recommendation System using Cosine Similarity and KNN,” Article in International Journal of Engineering and Advanced Technology, no. 9, pp. 2249–8958, 2020, doi: 10.35940/ijeat.E9666.069520.

N. Bhalse and R. Thakur, “Algorithm for movie recommendation system using collaborative filtering,” Mater Today Proc, Feb. 2021, doi: 10.1016/j.matpr.2021.01.235.

G. Liu and X. Wu, “Using Collaborative Filtering Algorithms Combined with Doc2Vec for Movie Recommendation,” in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Mar. 2019, pp. 1461–1464. doi: 10.1109/ITNEC.2019.8729076.

R. Ji, Y. Tian, and M. Ma, “Collaborative Filtering Recommendation Algorithm Based on User Characteristics,” in 2020 5th Interna-tional Conference on Control, Robotics and Cybernetics, CRC 2020, Oct. 2020, pp. 56–60. doi: 10.1109/CRC51253.2020.9253466.

C.-S. M. Wu, D. Garg, and U. Bhandary, “Movie Recommendation System Using Collaborative Filtering,” in 2018 IEEE 9th Interna-tional Conference on Software Engineering and Service Science (ICSESS), Nov. 2018, pp. 11–15. doi: 10.1109/ICSESS.2018.8663822.

G. Geetha, M. Safa, C. Fancy, and D. Saranya, “A Hybrid Approach using Collaborative filtering and Content based Filtering for Rec-ommender System,” in Journal of Physics: Conference Series, Apr. 2018, vol. 1000, no. 1. doi: 10.1088/1742-6596/1000/1/012101.

R. Chen, Q. Hua, Y. S. Chang, B. Wang, L. Zhang, and X. Kong, “A survey of collaborative filtering-based recommender systems: from traditional methods to hybrid methods based on social networks,” IEEE Access, vol. 6, pp. 64301–64320, 2018, doi: 10.1109/ACCESS.2018.2877208.

Z. Zhao and J. Zhang, “Weighted Slope One Algorithm Optimization Based on User Similarity and Item Similarity,” in 2018 14th In-ternational Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Jul. 2018, pp. 34–39. doi: 10.1109/FSKD.2018.8686857.

A. Tripathi and A. K. Sharma, “Recommending Restaurants: A Collaborative Filtering Approach,” in 2020 8th International Confer-ence on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Jun. 2020, pp. 1165–1169. doi: 10.1109/ICRITO48877.2020.9197946.

S. Linda and K. K. Bharadwaj, “A decision tree based context-aware recommender system,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11278 LNCS, pp. 293–305. doi: 10.1007/978-3-030-04021-5_27.

A. A. Fakhri, Z. K. A. Baizal, and E. B. Setiawan, “Restaurant Recommender System Using User-Based Collaborative Filtering Ap-proach: A Case Study at Bandung Raya Region,” in Journal of Physics: Conference Series, May 2019, vol. 1192, no. 1. doi: 10.1088/1742-6596/1192/1/012023.