Recommender systems play a vital role in various online services. However, the insulated
nature of training and deploying separately within a specific domain limits their access to …

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 …

Modern recommender systems learn user representations from historical interactions, which
suffer from the problem of user feature shifts, such as an income increase. Historical …

With the increase of content pages and interactive buttons in online services such as online-
shopping and video-watching websites, industrial-scale recommender systems face …

The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …

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 …

The recent surge in the field of Large Language Models (LLMs) has gained significant
attention in numerous domains. In order to tailor an LLM to a specific domain such as a web …

Feature interaction has been recognized as an important problem in machine learning,
which is also very essential for click-through rate (CTR) prediction tasks. In recent years …

Adapters, a plug-in neural network module with some tunable parameters, have emerged as
a parameter-efficient transfer learning technique for adapting pre-trained models to …

Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide
range of target tasks has become the de facto paradigm in many machine learning (ML) …