Comprehensively charting the biologically causal circuits that govern the phenotypic space
of human cells has often been viewed as an insurmountable challenge. However, in the last …

Recommender system (RS) has emerged as a major research interest that aims to help
users to find items online by providing suggestions that closely match their interest. This …

Cold-start recommendation has been a challenging problem due to sparse user-item
interactions for new users or items. Existing efforts have alleviated the cold-start issue to …

A common challenge for most current recommender systems is the cold-start problem. Due
to the lack of user-item interactions, the fine-tuned recommender systems are unable to …

Investigating active learning, we focus on the relation between the number of labeled
examples (budget size), and suitable querying strategies. Our theoretical analysis shows a …

Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …

Social recommender systems (SRS) aim to study how social relations influence users'
choices and how to use them for better learning users embeddings. However, the diversity of …

Highly skewed long-tail item distribution is very common in recommendation systems. It
significantly hurts model performance on tail items. To improve tail-item recommendation …

Deep active learning aims to reduce the annotation cost for the training of deep models,
which is notoriously data-hungry. Until recently, deep active learning methods were …

Graph Neural Networks (GNNs) have attracted much attention due to their superior learning
capability. Despite the successful applications of GNNs in many areas, their performance …