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RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms., , , , , , , , , and 9 other author(s). CIKM, page 4653-4664. ACM, (2021)Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering., , , , and . SIGIR, page 122-132. ACM, (2022)Can Small Language Models be Good Reasoners for Sequential Recommendation?, , , , , , , , and . WWW, page 3876-3887. ACM, (2024)ReLand: Integrating Large Language Models' Insights into Industrial Recommenders via a Controllable Reasoning Pool., , , , , , , , , and . RecSys, page 63-73. ACM, (2024)Periodicity May Be Emanative: Hierarchical Contrastive Learning for Sequential Recommendation., , , , and . CIKM, page 2442-2451. ACM, (2023)RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms., , , , , , , , , and 9 other author(s). CoRR, (2020)Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning., , , and . WWW, page 2320-2329. ACM, (2022)RecBole 2.0: Towards a More Up-to-Date Recommendation Library., , , , , , , , , and 9 other author(s). CIKM, page 4722-4726. ACM, (2022)Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data., , , , , , and . KDD, page 2154-2164. ACM, (2021)Temporal Contrastive Pre-Training for Sequential Recommendation., , , , and . CIKM, page 1925-1934. ACM, (2022)