Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

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

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

Cold-start problem is still a very challenging problem in recommender systems. Fortunately,
the interactions of the cold-start users in the auxiliary source domain can help cold-start …

In order to support the study of recent advances in recommender systems, this paper
presents an extended recommendation library consisting of eight packages for up-to-date …

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

Cold-start problems are enormous challenges in practical recommender systems. One
promising solution for this problem is cross-domain recommendation (CDR) which …

In a large recommender system, the products (or items) could be in many different
categories or domains. Given two relevant domains (eg, Book and Movie), users may have …

The conventional single-target Cross-Domain Recommendation (CDR) only improves the
recommendation accuracy on a target domain with the help of a source domain (with …

Providing accurate recommendations to newly joined users (or potential users, so-called
cold-start users) has remained a challenging yet important problem in recommender …