A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
Cross-domain recommendation: challenges, progress, and prospects
Causal inference in recommender systems: A survey and future directions
Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …
recommender systems extract user preferences based on the correlation in data, such as …
RecBole 2.0: towards a more up-to-date recommendation library
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 …
presents an extended recommendation library consisting of eight packages for up-to-date …
A survey on cross-domain recommendation: taxonomies, methods, and future directions
Traditional recommendation systems are faced with two long-standing obstacles, namely
data sparsity and cold-start problems, which promote the emergence and development of …
data sparsity and cold-start problems, which promote the emergence and development of …
Transfer-meta framework for cross-domain recommendation to cold-start users
Cold-start problems are enormous challenges in practical recommender systems. One
promising solution for this problem is cross-domain recommendation (CDR) which …
promising solution for this problem is cross-domain recommendation (CDR) which …
CATN: Cross-domain recommendation for cold-start users via aspect transfer network
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 …
categories or domains. Given two relevant domains (eg, Book and Movie), users may have …
Contrastive cross-domain recommendation in matching
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 …
target domain with the help of the source domain, which is widely used and explored in real …
Transfer learning
SJ Pan - Learning, 2020 - api.taylorfrancis.com
Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …
various real-world applications. However, most existing supervised algorithms work well …