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Real-world recommendation systems commonly offer diverse content scenarios for users to interact with.
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X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, “Lightgcn: Simplifying and powering graph convolution network for recommendation,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 639–648. [Online]. Available: https://doi.org/10.1145/3397271.3401063
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I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras, and I. Androutsopoulos, “LEGAL-BERT: The muppets straight out of law school,” in Findings of the Association for Computational Linguistics: EMNLP 2020 , T. Cohn, Y. He, and Y. Liu, Eds. Online: Association for Computational Linguistics, Nov. 2020, pp. 2898–2904. [Online]. Available: https://aclanthology.org/2020.findings-emnlp.261
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2021
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2021
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R. Wang, R. Shivanna, D. Cheng, S. Jain, D. Lin, L. Hong, and E. Chi, “Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems,” in Proceedings of the Web Conference 2021 , ser. WWW ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 1785–1797. [Online]. Available: https://doi.org/10.1145/3442381.3450078
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2022
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2022
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J. Lin, Y. Qu, W. Guo, X. Dai, R. Tang, Y. Yu, and W. Zhang, “Map: A model-agnostic pretraining framework for click-through rate prediction,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , ser. KDD ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 1384–1395. [Online]. Available: https://doi.org/10.1145/3580305.3599422
2023
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C. Wu, C. Wang, J. Xu, Z. Fang, T. Gu, C. Wang, Y. Song, K. Zheng, X. Wang, and G. Zhou, “ Instant Representation Learning for Recommendation over Large Dynamic Graphs ,” in 2023 IEEE 39th International Conference on Data Engineering (ICDE) . Los Alamitos, CA, USA: IEEE Computer Society, Apr. 2023, pp. 82–95. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/ICDE55515.2023.00014
2023
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Y. Bang, S. Cahyawijaya, N. Lee, W. Dai, D. Su, B. Wilie, H. Lovenia, Z. Ji, T. Yu, W. Chung, Q. V. Do, Y. Xu, and P. Fung, “A multitask, multilingual, multimodal evaluation of ChatGPT on reasoning, hallucination, and interactivity,” in Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) , J. C. Park, Y. Arase, B. Hu, W. Lu, D. Wijaya, A. Purwarianti, and A. A. Krisnadhi, Eds. Nusa Dua, Bali: Association for Computational Linguistics, Nov. 2023, pp. 675–718. [Online]. Available: https://aclanthology.org/2023.ijcnlp-main.45
2023
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S. Yang, C. Wang, Y. Liu, K. Xu, W. Ma, Y. Liu, M. Zhang, H. Zeng, J. Feng, and C. Deng, “Collaborative word-based pre-trained item representation for transferable recommendation,” in 2023 IEEE International Conference on Data Mining (ICDM) . IEEE, 2023, pp. 728–737
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J. Wang, Z. Zeng, Y. Wang, Y. Wang, X. Lu, T. Li, J. Yuan, R. Zhang, H.-T. Zheng, and S.-T. Xia, “Missrec: Pre-training and transferring multi-modal interest-aware sequence representation for recommendation,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023, pp. 6548–6557
2023
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J. Zhai, Z. Gong, Y. Wang, X. Sun, Z. Yan, F. Li, and X. Liu, “Revisiting neural retrieval on accelerators,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , ser. KDD ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 5520–5531. [Online]. Available: https://doi.org/10.1145/3580305.3599897
2023
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J. Zheng, H. Gu, C. Song, D. Lin, L. Yi, and C. Chen, “Dual interests-aligned graph auto-encoders for cross-domain recommendation in wechat,” ser. CIKM ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 4988–4994. [Online]. Available: https://doi.org/10.1145/3583780.3614676
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L. Zhang, X. Zhou, Z. Zeng, and Z. Shen, “Are id embeddings necessary? whitening pre-trained text embeddings for effective sequential recommendation,” 2024 IEEE 40th International Conference on Data Engineering (ICDE) , pp. 530–543, 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:267740149
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