Movie Recommendation System Using Deep Learning
Lakshit Sama, Hua Wang, Aaisha Makkar · 2021
It is a challenge to design a movie recommendation system with artificial intelligent technology. It is difficult to get satisfactory results as not all data is reliable and valuable from the internet. To solve this problem, researchers recommend using a recommendation system which presents relevant information instead of searching the same information multiple times. Generally, collaborative filtering methods based on user information (such as gender type, geographical area, or preference) are effective. However, in today’s era, everyone is concerned about suggestions or guidelines so that he/she can make the decision, our area of research includes movies recommendation system by which we can provide the list of similar movies to the user. Now the user is assisted with some lists of movies recommended to the user by our model, he/she only has to decide which movie he should watch next. Instead of looking into the whole list it’s better to have some recommendations based on which he/she can decide easily. The overall performance for the purposed framework resulted in an accuracy of 66% which is good for such initiative.