In recommendation systems, new items are continuously introduced, initially lacking
interaction records but gradually accumulating them over time. Accurately predicting the …

Cold-start problem is one of the long-standing challenges in recommender systems,
focusing on accurately modeling new or interaction-limited users or items to provide better …

The cold-start problem is a long-standing problem in recommender systems, ie, lack of
historical interaction information hinders effective recommendations for new users and …

Embedding-based recommender systems rely on historical interactions to model users,
which poses challenges for recommending to new users, known as the user cold-start …

The scaling law, which involves the brute-force expansion of training datasets and learnable
parameters, has become a prevalent strategy for developing more robust learning models …

Meta-learning has been widely employed to tackle the cold-start problem in user modeling.
Similar to a guidebook for a new traveler, meta-learning significantly affects decision-making …

Recommendation systems typically rely on users' historical behavior to infer their
preferences. However, when new entries emerge, the system cannot make accurate …

The recommendation system has far-reaching significance and great practical value, which
alleviates people's troubles about choosing from a huge amount of information. The existing …

Objectives: Sequential recommendation aims to recommend items that are relevant to users'
interests based on their existing interaction sequences. Current models lack in capturing …

The brute‐force scaleup of training datasets, learnable parameters and computation power,
has become a prevalent strategy for developing more robust learning models. However, due …