{"total":18,"rows":[{"id":"2401.13923","title":"Towards 3D Molecule-Text Interpretation in Language Models","created":"2024-01-25","cited_by_count":66,"area":null},{"id":"2404.12008","title":"How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective","created":"2024-04-18","cited_by_count":21,"area":null},{"id":"2407.05441","title":"Language Representations Can be What Recommenders Need: Findings and Potentials","created":"2024-07-07","cited_by_count":18,"area":null},{"id":"2407.15620","title":"Dual Test-time Training for Out-of-distribution Recommender System","created":"2024-07-22","cited_by_count":16,"area":null},{"id":"2310.10696","title":"Robust Collaborative Filtering to Popularity Distribution Shift","created":"2023-10-16","cited_by_count":12,"area":null},{"id":"2408.00490","title":"Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation","created":"2024-08-01","cited_by_count":11,"area":null},{"id":"2405.20718","title":"Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias","created":"2024-05-31","cited_by_count":9,"area":null},{"id":"2411.00163","title":"PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation","created":"2024-10-31","cited_by_count":9,"area":null},{"id":"2506.05736","title":"Generalized Incremental Learning under Concept Drift across Evolving Data Streams","created":"2025-06-06","cited_by_count":7,"area":null},{"id":"2407.17476","title":"ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems","created":"2024-06-28","cited_by_count":6,"area":null},{"id":"2505.00242","title":"D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams","created":"2025-05-01","cited_by_count":4,"area":null},{"id":"2404.02719","title":"Can We Understand Plasticity Through Neural Collapse?","created":"2024-04-03","cited_by_count":2,"area":null},{"id":"2501.04762","title":"Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations","created":"2025-01-08","cited_by_count":2,"area":null},{"id":"2506.15120","title":"Advancing Loss Functions in Recommender Systems: A Comparative Study with a R\\'enyi Divergence-Based Solution","created":"2025-06-18","cited_by_count":2,"area":null},{"id":"2505.14310","title":"Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity","created":"2025-05-20","cited_by_count":0,"area":null},{"id":"2606.21911","title":"The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders","created":"2026-06-20","cited_by_count":0,"area":null},{"id":"2608.03647","title":"Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2609.02162","title":"GenCAR: Generative Counterfactual Alignment with Risk-Controlled Selection for Out-of-Distribution Recommendation","created":"2026-09-02","cited_by_count":0,"area":null}],"next_cursor":null}