Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …

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 …

Nowadays, most e-commerce and entertainment services have adopted interactive
Recommender Systems (RS) to guide the entire journey of users into the system. This task …

Hyper-parameters tuning is a crucial task to make a model perform at its best. However,
despite the well-established methodologies, some aspects of the tuning remain unexplored …

Items popularity is a strong signal in recommendation algorithms. It strongly affects
collaborative filtering approaches and it has been proven to be a very good baseline in …

Modeling the temporal context efficiently and effectively is essential to provide useful
recommendations to users. In this work, we focus on improving neighborhood-based …

Venue recommendation approaches have become particularly useful nowadays due to the
increasing number of users registered in location-based social networks (LBSNs) …

Nowadays, instead of the traditional batch paradigm where the system trains and predicts a
model at scheduled times, new Recommender Systems (RSs) have become interactive …

In recommendation scenarios, similarity measures play a fundamental role in memory-
based nearest neighbors approaches. In fact, they recommend items to a user based on the …

Recommending sustainable products to the target users in a timely manner is the key drive
for consumer purchases in online stores and served as the most effective means of user …