A Collaborative Filtering Movie Recommendation System Based on Users Correlation and Weighted K-Means with High Accuracy
Subject Areas :Nozar Ebrahimi Lame 1 , Fatemeh saghafi 2 , Majid Gholipour 3
1 - PhD. Student in IT Management, Islamic Azad University, Qazvin Branch, Qazvin, Iran
2 - Associate Prof. of University of Tehran
3 - Faculty Member of Islamic Azad University, Qazvin Branch
Keywords: Recommendation Systems, Content Based filtering, Collaborative filtering, movie,
Abstract :
A Recommendation system is a BI tool that it uses data mining methods for guiding and helping the user to select the best items based on her/his preferences and in the shortest time. Despite more than two decades of academic research on recommendation systems, this issue is still one of the most up-to-date research challenges. Recommendation systems save the users time, increase their satisfaction and their loyalty to sales sites and lead to the development of e-commerce, by personalizing the recommendations of goods or services to site users. Nowadays, recommendation systems have many applications in various sectors of e-commerce, especially in media products such as books, movies, and music. The famous e-commerce sites such as eBay, Amazon, and Netflix and domestic sites such as Digikala, Divar, and Filimo widely use recommendation systems. These systems use a variety of big data filtering methods to provide appropriate recommendations. The most important and widely used filtering method is collaborative filtering (CF). In this paper, we implement three CF recommender systems based on the correlation coefficient between users, selecting the optimal number of neighbors and calculating weighted scores for unwatched movies. The best method with the least error is selected as the desired model. We use Movielens ml-latest-small 100k research dataset with 9742 movies and 610 users as input. The results showed 3.29% less RMSE error compared with the latest research that has used the correlation method.
[1] H. Øverby and Jan A. Audestad, 2018, Digital Economics. .
[2] P. M. Alamdari, N. J. Navimipour, M. Hosseinzadeh, A. A. Safaei, and A. Darwesh, “A Systematic Study on the Recommender Systems in the E-Commerce,” IEEE Access, vol. 8, pp. 115694–115716, 2020, doi: 10.1109/ACCESS.2020.3002803.
[3] Y. Zhang, H. Abbas, and Y. Sun, “Smart e-commerce integration with recommender systems,” Electronic Markets, vol. 29, no. 2. pp. 219–220, 2019, doi: 10.1007/s12525-019-00346-x.
[4] G. Lekakos and P. Caravelas, “A hybrid approach for movie recommendation,” Multimed. Tools Appl., vol. 36, no. 1–2, pp. 55–70, 2008, doi: 10.1007/s11042-006-0082-7.
[5] S. K. Raghuwanshi and R. K. Pateriya, “Recommendation systems: Techniques, challenges, application, and evaluation,” in Advances in Intelligent Systems and Computing, vol. 817, 2019, pp. 151–164.
[6] Aggarwal, C.C., Recommender systems. Vol. 1. 2016: Springer
[7] R. Singla, S. Gupta, A. Gupta, and D. K. Vishwakarma, “FLEX: A content based movie recommender,” 2020 Int. Conf. Emerg. Technol. INCET 2020, pp. 8–11, 2020, doi: 10.1109/INCET49848.2020.9154163.
[8] R. Ahuja, A. Solanki, and A. Nayyar, “Movie Recommender System Using K-Means Clustering AND K-Nearest Neighbor(2019).pdf,” Proc. 9th Int. Conf. Cloud Comput. Data Sci. Eng. Conflu. 2019, pp. 263–268, 2019, doi: 10.1109/CONFLUENCE.2019.8776969.
[9] G. Geetha, M. Safa, C. Fancy, and D. Saranya, “A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System,” J. Phys. Conf. Ser., vol. 1000, no. 1, 2018, doi: 10.1088/1742-6596/1000/1/012101.
[10] S. M. Choi, S. K. Ko, and Y. S. Han, “A movie recommendation algorithm based on genre correlations,” Expert Syst. Appl., vol. 39, no. 9, pp. 8079–8085, 2012, doi: 10.1016/j.eswa.2012.01.132.
[11] en.wikipedia.org/wiki/Netflix_prize
[12] https://aparat.design/recommendation-systems-in-filimo-cqzge4gfsevi
[13] S. M. Choi, S. K. Ko, and Y. S. Han, “A movie recommendation algorithm based on genre correlations,” Expert Syst. Appl., vol. 39, no. 9, pp. 8079–8085, 2012, doi: 10.1016/j.eswa.2012.01.132.
[14] Z. T. Jian Wei a , Jianhua He a , ∗, Kai Chen b , Yi Zhou c, “Collaborative filtering and deep learning based recommendation system for cold start items.” 2016.
[15]حیدری, ب.، پروین نیا,ا.، 1396، ارائه مدلی برای سیستم های توصیه گر فیلم مبتنی بر رویکرد مشارکت محور، مجله فناوری اطلاعات در طراحی مهندسی، دوره 10، شماره 1، شهریور 1396، صفحه 1 تا 9
[16] Notley, S. and M. Magdon-Ismail, Examining the use of neural networks for feature extraction: A comparative analysis using deep learning, support vector machines, and k-nearest neighbor classifiers. arXiv preprint arXiv:1805.02294, 2018
[17] S. Reddy, S. Nalluri, S. Kunisetti, S. Ashok, and B. Venkatesh, “Content-based movie recommendation system using genre correlation,” in Smart Innovation, Systems and Technologies, 2019, vol. 105, pp. 391–397, doi: 10.1007/978-981-13-1927-3_42.
[18] Behera, D.K., M. Das, and S. Swetanisha, Predicting Users’ Preferences for Movie Recommender System Using Restricted Boltzmann Machine, in Computational Intelligence in Data Mining. 2019, Springer. p. 759-769
[19] C. H. Lin and H. Chi, A Novel Movie Recommendation System Based on Collaborative Filtering and Neural Networks, vol. 926. Springer International Publishing, 2020.
[20] Furtado, F. and A. Singh, Movie recommendation system using machine learning. International Journal of Research in Industrial Engineering, 2020. 9(1): p. 84-98
[21] X. Li, H. Zhao, Z. Wang, and Z. Yu, “Research on Movie Rating Prediction Algorithms,” 2020 5th IEEE Int. Conf. Big Data Anal. ICBDA 2020, pp. 121–125, 2020, doi: 10.1109/ICBDA49040.2020.9101282.
[22] D. Lee and K. Hosanagar, “Impact of recommender systems on sales volume and diversity,” 35th Int. Conf. Inf. Syst. "Building a Better World Through Inf. Syst. ICIS 2014, pp. 1–15, 2014.
[23] F. Ricci, L. Rokach, B. Shapira, P. B. Kantor, and F. Ricci, Recommender Systems Handbook. 2011.
[24] K. Falk, “Practical Recommender Systems.” p. 406, 2019.
[25]حسینی، م.؛ نصرالهی، م.؛ بقایی، ع.1397، یک سامانه توصیه گر ترکیبی با استفاده از اعتماد و خوشه بندی دو جهته به منظور افزایش کارائی پالایش گروهی
[26] صابری، ن.؛ منتظر، غ. 1389، شخصي سازي محيط يادگيري الكترونيكي به كمك توصيه گر فازي مبتني برتلفيق سبك يادگيري و سبك شناختي، فصلنامه علمی-پژوهشی فناوری اطلاعات و ارتباطات ایران، سال دوم، شماره های 3و4
[27] Widiyaningtyas, T., Hidayah, I., & Adji, T. B. (2021). User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00425-x