Influence maximization, defined as a problem of finding a set of seed nodes to trigger a
maximized spread of influence, is crucial to viral marketing on social networks. For practical viral …

Influence maximization, fundamental for word-of-mouth marketing and viral marketing, aims
to find a set of seed nodes maximizing influence spread on social network. Early methods …

Post-click conversion, as a strong signal indicating the user preference, is salutary for building
recommender systems. However, accurately estimating the post-click conversion rate (…

Retrieval is a crucial stage in web search that identifies a small set of query-relevant candidates
from a billion-scale corpus. Discovering more semantically-related candidates in the …

Graph Neural Networks (GNNs) have evolved to understand graph structures through
recursive exchanges and aggregations among nodes. To enhance robustness, self-supervised …

The problem of data sparsity has long been a challenge in recommendation systems, and
previous studies have attempted to address this issue by incorporating side information. …

Recommender systems have seen significant advancements with the influence of deep
learning and graph neural networks, particularly in capturing complex user-item relationships. …

Heat stress results in cardiac dysfunction and even cardiac failure. To elucidate the cellular
and molecular mechanism of cardiomyocyte injury induced by heat stress, the changes of …

As the heart of a search engine, the ranking system plays a crucial role in satisfying users'
information demands. More recently, neural rankers fine-tuned from pre-trained language …

Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval
stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their …