The collaborative filtering is the most used technique for recommender systems. One of the main components of a recommender system based on the collaborative filtering technique, is the similarity measure used to determine the set of users having the same behavior with regard to the selected items.
> S. S. Devi and G. Parthasarathy, “A Hybrid Approach for Movie Recommendation System Using Feature Engineering”, 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, 2018, pp. 378--382
> Zan Wang, Xue Yu, Nan Feng, Zhenhua Wang, “An improved collaborative movie recommendation system using computational intelligence", Journal of Visual Languages & Computing, Volume 25, Issue 6, 2014, Pages 667--675, ISSN 1045-926X.
> C. M. Wu, D. Garg and U. Bhandary, “Movie Recommendation System Using Collaborative Filtering”, 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 2018, pp. 11--15.
> M. K. Kharita, A. Kumar and P. Singh, “Item-Based Collaborative Filtering in Movie Recommendation in Real time”, 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, India, 2018, pp. 340--342
> Jinbo Zhang, Zhiqing Lin, Bo Xiao and Chuang Zhang, "An optimized item-based collaborative filtering recommendation algorithm". 2009 IEEE International Conference on Network Infrastructure and Digital Content, Beijing, 2009, pp. 414--418