In streaming platforms, recommendation algorithms play a crucial role in recommending content. For streaming movie services such as Netflix, recommendation algorithms are crucial for assisting consumers in discovering new films to watch. Thus, in this paper, we present a deep learning strategy based on Convolutional Neural Networks (CNN) to generate a collaborative filtering system that predicts a user's movie rating using a big database of ratings from other users. The findings that were achieved by the Movie Recommendation System utilizing CNNs on the MovieLens 100k dataset highlight the usefulness of CNNs in capturing the complex and non-linear interactions that occur between users and movies."Science and innovation" international scient...
Technology has evolved a lot from basic to advanced such as Machine learning, deep learning, Interne...
This thesis covers the topic of utilizing neural nets for recommending movies. The principle of usin...
According to the expansion of users and the variety of products in the World Wide Web, users have be...
Recommender systems help people make decisions. They are particularly useful for product recommendat...
With the advancements of big data, recommendation systems have become extremely useful in wide appli...
Recommendation systems, the best way to deal with information overload, are widely utilized to provi...
With the development of the entertainment and film industry, people have more chances to access movi...
Movie recommendation system has become a key part in online movie services to gain and maintain the ...
With the development of the network, society has moved into the data era, and the amount of data is ...
Movie recommendation systems are becoming increasingly popular, with many businesses looking to leve...
Most of the recommender systems are built for the content or item providers. For example, Netflix...
Recommendation is an ideology that works as choice-based system for the end users. Users are recomme...
A recommendation system is a system that provides online users with recommendations for particular r...
In the current era, a rapid increase in data volume produces redundant information on the internet. ...
Over the past years, the internet has broadened the horizon of various domains to interact and share...
Technology has evolved a lot from basic to advanced such as Machine learning, deep learning, Interne...
This thesis covers the topic of utilizing neural nets for recommending movies. The principle of usin...
According to the expansion of users and the variety of products in the World Wide Web, users have be...
Recommender systems help people make decisions. They are particularly useful for product recommendat...
With the advancements of big data, recommendation systems have become extremely useful in wide appli...
Recommendation systems, the best way to deal with information overload, are widely utilized to provi...
With the development of the entertainment and film industry, people have more chances to access movi...
Movie recommendation system has become a key part in online movie services to gain and maintain the ...
With the development of the network, society has moved into the data era, and the amount of data is ...
Movie recommendation systems are becoming increasingly popular, with many businesses looking to leve...
Most of the recommender systems are built for the content or item providers. For example, Netflix...
Recommendation is an ideology that works as choice-based system for the end users. Users are recomme...
A recommendation system is a system that provides online users with recommendations for particular r...
In the current era, a rapid increase in data volume produces redundant information on the internet. ...
Over the past years, the internet has broadened the horizon of various domains to interact and share...
Technology has evolved a lot from basic to advanced such as Machine learning, deep learning, Interne...
This thesis covers the topic of utilizing neural nets for recommending movies. The principle of usin...
According to the expansion of users and the variety of products in the World Wide Web, users have be...