In our work, we have presented two widely used recommendation systems. We have
presented a context-aware recommender system to filter the items associated with user's …

Recommender systems use algorithms to provide users with product or service
recommendations. Recently, these systems have been using machine learning algorithms …

“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 …

A recommender system (RS) aims to provide personalized recommendations to users for
specific items (eg, music, books). Popular techniques involve content-based (CB) models …

In collaborative filtering recommender systems user's preferences are expressed as ratings
for items, and each additional rating extends the knowledge of the system and affects the …

Recommender systems have the effect of guiding users in a personalized way to interesting
objects in a large space of possible options. Content-based recommendation systems try to …

Among collaborative recommendation approaches, methods based on nearest-neighbors
still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability …

Content-based recommender systems (CBRSs) rely on item and user descriptions (content)
to build item representations and user profiles that can be effectively exploited to suggest …

Among collaborative recommendation approaches, methods based on nearest-neighbors
still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability …

This paper investigates the use of automatically extracted visual features of videos in the
context of recommender systems and brings some novel contributions in the domain of …