Abstract
Recommendation is an ideology that works as choice-based system for the end users. Users are recommended with their favorite movies based on history of other watched movies or based on the category of the movies. These types of recommendations are becoming popular because of their ability to think and react as human brain. For this purpose, deep learning or artificial intelligence comes into picture. It is the ability to think as a human brain as give the output best suited to the end users liking. This paper focuses on implementing the recommendation system of movies using deep learning with neural network model using the activation function of SoftMax to give an experience to users as friendly recommendation. Moreover, this paper focuses on different scenarios of recommendation like the recommendation based on history, genre of the movie etc.
Highlights
Recommendation systems are highly popular in the field of entertainment like movies, songs, clothes, accessories, food, restaurants etc
The recommendations done in this paper are made using deep learning that considers a favorite movie of the user and recommends similar ten movies based on keywords, cast, director, genre and popularity of the movie
The dataset used for this project consists of 40,000 Hollywood movie records consisting of the attributes such as ID, Title, Release_Date, Director, Cast, Popularity, Keywords, Genre and many more attributes
Summary
Recommendation systems are highly popular in the field of entertainment like movies, songs, clothes, accessories, food, restaurants etc. For any recommendation system to work properly it should know its audience Some of such highly popular recommendation websites are Netflix, YouTube, Amazon, Flip kart, Hulu etc. These systems have taken user understanding and prediction to a great level. In collaborative filtering user with similar interests are found out from a large group of users. In content-based filtering, liking of only one particular user is taken into consideration. The recommendations done in this paper are made using deep learning that considers a favorite movie of the user and recommends similar ten movies based on keywords, cast, director, genre and popularity of the movie. It recommends the top ten movies based on the genre (or category) of the movie which uses popularity as the major aspect to recommend the movies
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