AN ADVANCED MOVIE RECOMMENDER ENGINE IMPLEMENTED IN PYTHON - MOLOKWU REGINALD CHUKWUKA (International Journal of Innovations in Engineering Research and Technology, 2021)
These days, we are living during a time of suggestion. Amazon remains on top of things in the online business industry by customizingsuggestion of things customers may like based on past requests; Trip Consultant gives diverse inn rankings to various clients; Youtubeshows "Related Articles" catch on video page to draw in clients; Netflix accomplishes 2/3 of its film sees by suggestions. Recommender frameworks have turned out to be universal in our lives. However, as of now, they are a long way from ideal. In this project, I explored two approachesof the Colaborative filtering method; theMemory-Based Collaborative filter by computing cosine similarityand the Model-based collaborative filtering using the singular value decomposition (SVD) tounderstand the different sectionof collaborative filteringand compare their performanceon thepopularMovieLens dataset. Which is one of the most common datasets used when implementing and testing recommender engines. It contains over 100 thousandmovie ratings ranging from 943users and a selection of 1682 movies. Execution results are displayed too as well as a discourse on future upgrades