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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
To address the long-standing data sparsity problem in recommender systems (RSs), cross-
domain recommendation (CDR) has been proposed to leverage the relatively richer …
domain recommendation (CDR) has been proposed to leverage the relatively richer …
Large language models are zero-shot rankers for recommender systems
Recently, large language models (LLMs)(eg, GPT-4) have demonstrated impressive general-
purpose task-solving abilities, including the potential to approach recommendation tasks …
purpose task-solving abilities, including the potential to approach recommendation tasks …
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
For a long time, different recommendation tasks require designing task-specific architectures
and training objectives. As a result, it is hard to transfer the knowledge and representations …
and training objectives. As a result, it is hard to transfer the knowledge and representations …
Personalized transfer of user preferences for cross-domain recommendation
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 …
the interactions of the cold-start users in the auxiliary source domain can help cold-start …
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 …
Disencdr: Learning disentangled representations for cross-domain recommendation
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 …
Domain Recommendation (CDR) has attracted a surge of interests, which utilizes the rich …
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 …
Cross-domain recommendation via user interest alignment
Cross-domain recommendation aims to leverage knowledge from multiple domains to
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …
Learning to warm up cold item embeddings for cold-start recommendation with meta scaling and shifting networks
Recently, embedding techniques have achieved impressive success in recommender
systems. However, the embedding techniques are data demanding and suffer from the cold …
systems. However, the embedding techniques are data demanding and suffer from the cold …