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Fast Adaptation for Cold-start Collaborative Filtering with Meta-learning., , , , , , , and . ICDM, page 661-670. IEEE, (2020)Learning to Learn the Future: Modeling Concept Drifts in Time Series Prediction., , , , and . CIKM, page 2434-2443. ACM, (2021)Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation., , , , , , , and . WWW, page 2216-2226. ACM, (2022)On the Theories Behind Hard Negative Sampling for Recommendation., , , , , , and . WWW, page 812-822. ACM, (2023)MaSS: Model-agnostic, Semantic and Stealthy Data Poisoning Attack on Knowledge Graph Embedding., , , , , , and . WWW, page 2000-2010. ACM, (2023)SoarGraph: Numerical Reasoning over Financial Table-Text Data via Semantic-Oriented Hierarchical Graphs., , , , , and . WWW (Companion Volume), page 1236-1244. ACM, (2023)Interpretable Fashion Matching with Rich Attributes., , , , , , and . SIGIR, page 775-784. ACM, (2019)Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis., , , , , , and . KDD, page 305-315. ACM, (2022)Causal Recommendation: Progresses and Future Directions., , , , , and . SIGIR, page 3432-3435. ACM, (2023)A Causal View for Item-level Effect of Recommendation on User Preference., , , , , and . WSDM, page 240-248. ACM, (2023)