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 provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

The cross-domain recommendation technique is an effective way of alleviating the data
sparse issue in recommender systems by leveraging the knowledge from relevant domains …

Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …

In the field of sequential recommendation, deep learning--(DL) based methods have
received a lot of attention in the past few years and surpassed traditional models such as …

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 …

The increasing popularity and diversity of social media sites has encouraged more and
more people to participate on multiple online social networks to enjoy their services. Each …

Next point-of-interest (POI) recommendation is a hot research field where a recent emerging
scenario, next POI to search recommendation, has been deployed in many online map …

As one promising way to solve the challenging issues of data sparsity and cold start in
recommender systems, crossdomain recommendation has gained increasing research …