With the increasing amount of information on the internet, recommendation system (RS) has
been utilized in a variety of fields as an efficient tool to overcome information overload. In …

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 …

“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 …

Recent technological advancements have led to a deluge of data from distinctive domains
(eg, health care and scientific sensors, user-generated data, Internet and financial …

Collaborative filtering (CF) based recommender systems are typically trained based on
personal interaction data (eg, clicks and purchases) that could be naturally represented as …

In recent years, recommendation systems have been widely used in various commercial
platforms to provide recommendations for users. Collaborative filtering algorithms are one of …

Personalizing user experience in recommender systems is possible when there is sufficient
information about the user. But when new users join the system, the unavailability of …

Co-clustering is to cluster samples and features simultaneously, which can also reveal the
relationship between row clusters and column clusters. Therefore, lots of scientists have …

Abstract Information extracted from social network services promise to improve the accuracy
of recommender systems in various domains. Against this background, community detection …

Neighborhood-based collaborative filtering algorithms, also referred to as memory-based
algorithms, were among the earliest algorithms developed for collaborative filtering. These …