Movie Recommendation System in R

Movie Recommendation System in R
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The movie and film industry has since the turn of the 20th century when it was established been the go to source for entertainment for many people around the world. Movie directors and producers have been phenomenal in the development of films that not only entertain but also address various societal matters and concerns. Over the years, the film industry has undergone significant transformations. It has gone from the period of silent films that were in black and white to ones with sound perfectly synchronized and colored pictures. Similarly, with advancements in technology, movie lovers don’t have to frequent cinema halls to catch their favorite shows and films showing as they can the stream them conveniently from their digital and computer devices. With access to the internet and companies such as Netflix and Amazon Prime offering streaming services, movie lovers can sit back and watch movies in different genres at the comfort of their living rooms. These streaming companies succeed in meeting the movie needs and preferences of different consumers through the recommendation system. The paper purports to examine how a recommendation system works, learn how it accepts the input of many categories and provide accurate results for users based on similarity ranking.
Background Information on the Recommendation System
Technological advancements have evidently swept through different industries around the globe including the film sector. While there are still movie lovers who enjoy the thrill of visiting cinema halls and theaters to catch up new films with family and friends, majority of individuals in society have embraced streaming services from companies such as Netflix and Amazon Prime. These organizations allow movie enthusiasts to binge watch their favorite shows at the comfort of their living rooms and all they need is a compute or digital device and access to reliable internet connection (Cong, 2019). These streaming platforms provide films in a wide range of genres and ensure that their lists are updated regularly with not only the latest releases but also timeless classics. With time upon using the streaming platforms such as Netflix, users start receiving recommendations on different TV movies and shows that are in line with a person’s tastes and preferences (DataFlair, 2020). As such, an individual who regularly engages Netflix for some action-packed films will get movie recommendations in that genre. Overall, these streaming organizations succeed in understanding consumer behavior and the movies and films that appeal to different individuals using the recommendation system. According to Cintia Ganesha Putri et al., (2020), “A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item”. The recommendation system tends to learn and understand a person’s watching pattern and consequently provide them with relevant suggestions.
As such, while individuals think that they often choose what to watch on Netflix, the actual reality is that their decision result from an algorithm based on the recommendation system. Algorithms are vital concepts in machine learning and artificial intelligence that are basically databased instructions that direct Netflix’s course of action (Hernández-Rubio et al., 2018). In this regard, every time an individual spends time to watch a movie on one of the streaming platforms such as Netflix, data is not only collected that informs the algorithm but also refreshes it. Consequently, the more films one watches the more updated the algorithm. With collected data, Netflix sees different aspects including the types of shows people watch including their timing and frequencies (Ibrahim et al., 2019). For instance, it is common for a person to watch a film severally. Considering that it is impossible for Netflix to offer the entire movie catalogue at its disposal, it has embraced the machine learning algorithm of similarity ranking in their curation attempts. Lytvyn et al., (2019) reiterates that position and shares that “As quality and taste are rarely the same thing, Netflix cannot work as Rotten T...