Recommender systems are effective tools of information filtering that are prevalent due to
increasing access to the Internet, personalization trends, and changing habits of computer …

Recommendation systems, the best way to deal with information overload, are widely
utilized to provide users with personalized content and services with high efficiency. Many …

With the ever‐increasing use of Internet and social networks that generate a vast amount of
information, there is a serious need for recommendation systems. In this article, we propose …

Nowadays, most of the websites like Amazon, YouTube and Netflix use collaborative filtering
methods to recommend various types of items to users. There are two principal categories of …

For the application of social item recommendation, how to effectively dig out the implicit
relationships between different items plays a crucial role in its performance. However …

Personalized recommendation is a technical means to help users quickly and efficiently
obtain interesting content from massive information. However, the traditional …

In this paper, we present a novel multimodal framework for video recommendation based on
deep learning. Unlike most common solutions, we formulate video recommendations by …

Content‐based (CB) and collaborative filtering (CF) recommendation algorithms are widely
used in modern e‐commerce recommender systems (RSs) to improve user experience of …

When a movie is uploaded to a movie Recommender System (eg, YouTube), the system can
exploit various forms of descriptive features (eg, tags and genre) in order to generate …

Deep learning one of the hottest technologies that applied in various field for enhancing the
available results which solving specific problems. The huge growth of the data over internet …