Recommender systems have emerged as the essential part of many e-commerce web sites. These systems provide personalized services to assist users in finding favorite items among the huge number of available media on the World Wide Web. Identifying temporal preferences of individuals is one of the major challenges of recommender systems to provide personalization for users. In this Content Based Recommendation System, It uses attributes such as genre, director, description, actors, etc. for movies, to make suggestions for the users. The intuition behind this sort of recommendation system is that if a user liked a particular movie or show, he/she might like a movie or a show similar to it.
Movies name
Movies Title and release date
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[1] Cami, B.R., Hassanpour, H. and Mashayekhi, H., 2017, December. A content-based movie recommender system based on temporal user preferences. In 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS) (pp. 121-125). IEEE. [2] Singla, R., Gupta, S., Gupta, A. and Vishwakarma, D.K., 2020, June. FLEX: a content based movie recommender. In 2020 International Conference for Emerging Technology (INCET) (pp. 1-4). IEEE.