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Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection.

, , , , , , and . ACL (industry), page 116-125. Association for Computational Linguistics, (2023)

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Neural Hierarchical Factorization Machines for User's Event Sequence Analysis., , , , , , , , and . SIGIR, page 1893-1896. ACM, (2020)Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting., , , , , , , and . CoRR, (2023)Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks., , , , , , , and . SIGIR, page 1167-1176. ACM, (2021)Interest Clock: Time Perception in Real-Time Streaming Recommendation System., , , , , and . SIGIR, page 2915-2919. ACM, (2024)Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection., , , , , , , and . AAAI, page 14957-14965. AAAI Press, (2021)Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion., , , , , , and . IJCAI, page 2248-2254. ijcai.org, (2022)Attacking Pre-trained Recommendation., , , , , , , and . SIGIR, page 1811-1815. ACM, (2023)MDFEND: Multi-domain Fake News Detection., , , , and . CIKM, page 3343-3347. ACM, (2021)Combat data shift in few-shot learning with knowledge graph., , , , , , and . Frontiers Comput. Sci., 17 (1): 171305 (2023)Self-Supervised learning for Conversational Recommendation., , , , , , and . Inf. Process. Manag., 59 (6): 103067 (2022)