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Item representation learning (IRL) plays an essential role in recommender systems, especially for sequential recommendation.
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S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan, “Session-based recommendation with graph neural networks,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 346–353
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F. Zhu, C. Chen, Y. Wang, G. Liu, and X. Zheng, “Dtcdr: A framework for dual-target cross-domain recommendation,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management , 2019, pp. 1533–1542
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J. Ni, J. Li, and J. McAuley, “Justifying recommendations using distantly-labeled reviews and fine-grained aspects,” in Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) , 2019, pp. 188–197
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2020
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Z. He, H. Zhao, Z. Lin, Z. Wang, A. Kale, and J. McAuley, “Locker: Locally constrained self-attentive sequential recommendation,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management , 2021, pp. 3088–3092
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2021
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2021
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2022
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Y. Hou, B. Hu, Z. Zhang, and W. X. Zhao, “Core: simple and effective session-based recommendation within consistent representation space,” in Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval , 2022, pp. 1796–1801
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2022
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K. Xu, Z. Wang, W. Zheng, Y. Ma, C. Wang, N. Jiang, and C. Cao, “A centralized-distributed transfer model for cross-domain recommendation based on multi-source heterogeneous transfer learning,” in 2022 IEEE International Conference on Data Mining (ICDM) . IEEE, 2022, pp. 1269–1274
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2023
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S. Mu, Y. Hou, W. X. Zhao, Y. Li, and B. Ding, “Id-agnostic user behavior pre-training for sequential recommendation,” in Information Retrieval: 28th China Conference, CCIR 2022, Chongqing, China, September 16–18, 2022, Revised Selected Papers . Springer, 2023, pp. 16–27
2023
Closest in time.
2023
Closest in time.