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Found 10 papers, 5 papers with code
KHANQ: A Dataset for Generating Deep Questions in Education
Designing in-depth educational questions is a time-consuming and cognitively demanding task.
Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage
Such perspectives are called “frames” in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article.
Discrete Semantic Tokenization for Deep CTR Prediction
Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.
Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision
Specifically, we introduce a novel method that enhances the learning of embeddings in SR through the supervision of modality correlations.
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation
Notably, the condensation paradigm of this method is forward and free from iterative optimization on the synthesized dataset.
Automatic Feature Fairness in Recommendation via Adversaries
We leverage the Adaptive Adversarial perturbation based on the widely-applied Factorization Machine (AAFM) as our backbone model.
A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems
However, not all CRS approaches use human conversations as their source of interaction data; the majority of prior CRS work simulates interactions by exchanging entity-level information.
Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems
We propose a graph-based approach (named MMSR) to fuse modality features in an adaptive order, enabling each modality to prioritize either its inherent sequential nature or its interplay with other modalities.
Do We Really Need Graph Neural Networks for Traffic Forecasting?
Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting.
Modeling and Leveraging Prerequisite Context in Recommendation
Prerequisites can play a crucial role in users' decision-making yet recommendation systems have not fully utilized such contextual background knowledge.
