6 accepted papers
Large Language Models (LLMs) have achieved remarkable success in recent years, owing to their impressive generalization capabilities and rich world knowledge. To capitalize on the potential of using LLMs as recommender systems, mainstream approaches typically focus on two paradigms. The first paradi
Multimodal sentiment analysis (MSA) has shown promising results but often poses significant challenges in real-world applications due to its dependence on the complete and aligned multimodal sequences. While existing approaches attempt to address missing modalities through feature reconstruction, th
In numerous user-centric services on mobile applications (apps), accurately mining user interests and generating effective user representations are paramount. Traditional approaches, which often involve training task-specific user representations, are becoming increasingly impractical due to their h…
User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent studies tackled this problem by pre-training the user model on massive user behavior sequences with a contrastive learning t…
Cognitive Diagnosis aims to quantify the proficiency level of students on specific knowledge concepts. Existing studies merely leverage observed historical students-exercise interaction logs to access proficiency levels. Despite effectiveness, observed interactions usually exhibit a power-law distri…
Cognitive psychology research shows that humans have the instinct for abstract thinking, where association plays an essential role in language comprehension. Especially for Chinese, its ideographic writing system allows radicals to trigger semantic association without the need of phonetics. In fact,…