We apply artificial neural networks trained using error back-propagation to construct three different systems for automated information filtering. These systems utilize content-based filtering, collaborative filtering, and a combination of both methods respectively. Extensive experimental evaluation of the systems was carried out with movie recommendation as a test domain, using data collected from our own on-line survey
Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendat...
Recommendation systems help consumers find useful items of information given a large amount of infor...
The massive amount of information available on the World Wide Web has made a requirement for busines...
The usage of Internet applications, such as social networking and e-commerce is increasing exponenti...
Recommender systems help people make decisions. They are particularly useful for product recommendat...
Abstract:- Most recommender systems use collaborative filtering or content-based methods to predict ...
With the proliferation of harmful Internet content such as pornography, violence, and hate messages,...
With the advancements of big data, recommendation systems have become extremely useful in wide appli...
Although content is fundamental to our music listening preferences, the leading performance in music...
To incorporate content information into collab-orative filtering methods, we train a neural net-work...
Neural collaborative filtering is the state of art field in the recommender systems area; it provide...
On the Internet, content filtering (also known as information filtering) is the use of a program to ...
Recommender Systems are information filtering engines used to estimate user preferences on items they...
In streaming platforms, recommendation algorithms play a crucial role in recommending content. For s...
Social networking platforms like, Twitter, Face book etc., have now emerged as a major forum for the...
Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendat...
Recommendation systems help consumers find useful items of information given a large amount of infor...
The massive amount of information available on the World Wide Web has made a requirement for busines...
The usage of Internet applications, such as social networking and e-commerce is increasing exponenti...
Recommender systems help people make decisions. They are particularly useful for product recommendat...
Abstract:- Most recommender systems use collaborative filtering or content-based methods to predict ...
With the proliferation of harmful Internet content such as pornography, violence, and hate messages,...
With the advancements of big data, recommendation systems have become extremely useful in wide appli...
Although content is fundamental to our music listening preferences, the leading performance in music...
To incorporate content information into collab-orative filtering methods, we train a neural net-work...
Neural collaborative filtering is the state of art field in the recommender systems area; it provide...
On the Internet, content filtering (also known as information filtering) is the use of a program to ...
Recommender Systems are information filtering engines used to estimate user preferences on items they...
In streaming platforms, recommendation algorithms play a crucial role in recommending content. For s...
Social networking platforms like, Twitter, Face book etc., have now emerged as a major forum for the...
Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendat...
Recommendation systems help consumers find useful items of information given a large amount of infor...
The massive amount of information available on the World Wide Web has made a requirement for busines...