A survey on popularity bias in recommender systems

A Klimashevskaia, D Jannach, M Elahi… - User Modeling and User …, 2024 - Springer
Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

[PDF][PDF] A survey on popularity bias in recommender systems

A Klimashevskaia, D Jannach, M Elahi… - arxiv preprint arxiv …, 2023 - christophtrattner.com
Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

DiffCL: A Diffusion-Based Contrastive Learning Framework with Semantic Alignment for Multimodal Recommendations

Q Song, J Hu, L **ao, B Sun, X Gao, S Li - arxiv preprint arxiv:2501.01066, 2025 - arxiv.org
Multimodal recommendation systems integrate diverse multimodal information into the
feature representations of both items and users, thereby enabling a more comprehensive …

Multimodal Graph Neural Network for Recommendation with Dynamic De-redundancy and Modality-Guided Feature De-noisy

F Mo, L **ao, Q Song, X Gao, E Liang - arxiv preprint arxiv:2411.01561, 2024 - arxiv.org
Graph neural networks (GNNs) have become crucial in multimodal recommendation tasks
because of their powerful ability to capture complex relationships between neighboring …

Rethinking Radiology Report Generation via Causal Reasoning and Counterfactual Augmentation

X Song, J Liu, Y Li, W Lei, R Wang - arxiv preprint arxiv:2311.13307, 2023 - arxiv.org
Radiology Report Generation (RRG) draws attention as an interaction between vision and
language fields. Previous works inherited the ideology of vision-to-language generation …