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 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 …

Data sparsity is a long-standing problem in recommender systems. To alleviate it, Cross-
Domain Recommendation (CDR) has attracted a surge of interests, which utilizes the rich …

Recommender systems have been widely deployed in many real-world applications, but
usually suffer from the long-standing user cold-start problem. As a promising way, Cross …

Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to
improve the target behavior's performance. We argue that MBR models should:(1) model the …

Cross-domain recommendation (CDR) aims to provide better recommendation results in the
target domain with the help of the source domain, which is widely used and explored in real …

Cross-domain recommendation aims to leverage knowledge from multiple domains to
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …

In industry, web platforms such as Alibaba and Amazon often provide diverse services for
users. Unsurprisingly, some developed services are data-rich, while some newly started …

Conversational recommender systems (CRS) aim to provide highquality recommendations
in conversations. However, most conventional CRS models mainly focus on the dialogue …

Cross-domain recommendation (CDR) aims at leveraging the correlation of users' behaviors
in both the source and target domains to improve the user preference modeling in the target …