Serendipitous recommendations have emerged as a compelling approach to deliver users
with unexpected yet valuable information, contributing to heightened user satisfaction and …

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 …

Recommending purely cold-start items is a long-standing and fundamental challenge in the
recommender systems. Without any historical interaction on cold-start items, the …

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 …

Predicting a user's preference in a short anonymous interaction session instead of long-term
history is a challenging problem in the real-life session-based recommendation, eg, e …

Recommender systems aim to provide item recommendations for users and are usually
faced with data sparsity problems (eg, cold start) in real-world scenarios. Recently pre …

Conventional zero-shot learning (ZSL) methods generally learn an embedding, eg, visual-
semantic mapping, to handle the unseen visual samples via an indirect manner. In this …

Sequential Recommendation characterizes the evolving patterns by modeling item
sequences chronologically. The essential target of it is to capture the item transition …

Recently, embedding techniques have achieved impressive success in recommender
systems. However, the embedding techniques are data demanding and suffer from the cold …

Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …