Recommender systems have been widely used in various domains including movies, news,
music with an aim to provide the most relevant proposals to users from a variety of available …

Over the years, the recommender systems (RS) have witnessed an increasing growth for its
enormous benefits in supporting users' needs through map** the available products to …

Recommender systems help users to find information that fits their preferences in an
overloaded search space. Collaborative filtering systems suffer from increasingly severe …

The ultimate goal of the Recommender System (RS) is to offer a proposal that is very close
to the user's real opinion. Data clustering can be effective in increasing the accuracy of …

In designing modern recommender systems, item feature information (or side information) is
often ignored as most models focus on exploiting rating information. However, the side …

Recommender Systems present a high-level of sparsity in their ratings matrices. The
collaborative filtering sparse data makes it difficult to: 1) compare elements using memory …

The way that users provide feedback on items regarding their satisfaction varies among
systems: in some systems, only explicit ratings can be entered; in other systems textual …

This paper proposes a class of temporal association rules, denoted by TACOs, for event
prediction. As opposed to previous graph rules, TACOs monitor updates to graphs, and can …

Recommendation systems have been widely deployed to address the challenge of
overwhelming information. They are used to enable users to find interesting information from …

Movie recommendation system offers a mechanism to allocate the user to attain the famous
film by getting an opinion from similar users or past rating by user. This produces …