This paper reviews the research trends that link the advanced technical aspects of
recommendation systems that are used in various service areas and the business aspects of …

Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …

Natural language processing (NLP) applications are now more powerful and ubiquitous
than ever before. With rapidly developing (neural) models and ever-more available data …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

Among different hybrid recommendation techniques, network-based entity recommendation
methods, which utilize user or item relationship information, are beginning to attract …

In a variety of application domains the content to be recommended to users is associated
with text. This includes research papers, movies with associated plot summaries, news …

Written by leaders in the data mining community, including the developers of the RapidMiner
software, this book provides an in-depth introduction to the application of data mining and …

Most existing recommender systems focus on modeling the ratings while ignoring the
abundant information embedded in the review text. In this paper, we propose a unified …

Motivation: The in silico prediction of potential interactions between drugs and target
proteins is of core importance for the identification of new drugs or novel targets for existing …

User modeling is an essential task for online recommender systems. In the past few
decades, collaborative filtering (CF) techniques have been well studied to model users' long …