To address the long-standing data sparsity problem in recommender systems (RSs), cross-
domain recommendation (CDR) has been proposed to leverage the relatively richer …

With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

Traditional recommendation systems are faced with two long-standing obstacles, namely
data sparsity and cold-start problems, which promote the emergence and development of …

Recommender systems have been widely applied to several domains and applications to
assist decision making by recommending items tailored to user preferences. One of the …

Music recommender systems (MRSs) have experienced a boom in recent years, thanks to
the emergence and success of online streaming services, which nowadays make available …

Recommender systems are software tools used to generate and provide suggestions for
items and other entities to the users by exploiting various strategies. Hybrid recommender …

Recommender systems use past behaviors of users to suggest items. Most tend to offer
items similar to the items that a target user has indicated as interesting. As a result, users …

Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

While the recent emergence of a large number of online course resources has made life
more convenient for many people, it has also caused information overload. According to a …

We propose a novel way to train ranking models, such as recommender systems, that are
both effective and efficient. Knowledge distillation (KD) was shown to be successful in image …