Generating accurate SQL from natural language questions (text-to-SQL) is a long-standing
challenge due to the complexities in user question understanding, database schema …

Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-
standing challenge for Graph Neural Networks (GNNs) due to the overwhelming …

Representing information of multiple behaviors in the single graph collaborative filtering
(CF) vector has been a long-standing challenge. This is because different behaviors …

Collaborative filtering (CF) models have demonstrated remarkable performance in
recommender systems, which represent users and items as embedding vectors. Recently …

Large language models (LLMs) have demonstrated remarkable capabilities in a wide range
of tasks, yet their application to specialized domains remains challenging due to the need for …

Dynamic graph data mining has gained popularity in recent years due to the rich information
contained in dynamic graphs and their widespread use in the real world. Despite the …

Graph collaborative filtering, which learns user and item representations through message
propagation over the user-item interaction graph, has been shown to effectively enhance …

Recommending cold items remains a significant challenge in billion-scale online
recommendation systems. While warm items benefit from historical user behaviors, cold …

Click-through rate (CTR) prediction refers to the estimation of the probability that a given
user will click on a given advertisement, with the objective of displaying items of greater …