随笔分类 - Recommender Systems
摘要：目录概Topology DistillationFull Topology Distillation (FTD)Hierarchical Topology Distillation (HTD)代码 Kang S., Hwang J., Kweon W. and Yu H. Topology dist
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摘要：目录概符号说明Collaborative distillation (CD) Lee J., Choi M., Lee J. and Shim H. Collaborative distillation for top-N recommendation. ICDM, 2019. 概 Ranking-
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摘要：目录概符号说明Ranking Distillation代码 Tang J. and Wang K. Ranking Distillation: Learning compact ranking models with high performance for recommender system.
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摘要：目录概符号说明TimelyRecMulti-aspect Time Encoder (MATE)Time-aware History Encoder (TAHE)Prediction代码 Cho J., Hyun D., Kang S. and Yu H. Learning heterogeneou
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摘要：目录概符号说明MOJITO代码 Tran V., Salha-Galvan G., Sguerra B. and Hennequin R. Attention mixtures for time-aware sequential recommendation. SIGIR, 2023. 概 本文希望
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摘要：[TOC] > [Cai X., Xia L., Ren X. and Huang C. How expressive are graph neural networks in recommendation? CIKM, 2023.](http://arxiv.org/abs/2308.11127)
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摘要：[TOC] > [Ye W., Wang S., Chen X., Wang X., Qin Z. and Yin D. Time Matters: Sequential recommendation with complex temporal information. SIGIR, 2020.](
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摘要：[TOC] > [Wu J., Cai R. and Wang H. D\'ej\`a vu: A contextualized temporal attention mechanism for sequential recommendation. WWW, 2020.](http://arxiv.
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摘要：[TOC] > [Fan Z., Liu Z., Zhang J., Xiong Y., Zheng L. and Yu P. S. Continuous-time sequential recommendation with temporal graph collaborative transfo
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摘要：# Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations [TOC] > [Huang L., Ma Y., Liu Y., Du B., Wang S. and Li
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摘要：[TOC] > [Zhao Y., Wang X., Chen J., Wang Y., Tang W., He X. and Xie H. Time-aware path reasoning on knowledge graph for recommendation. TOIS, 2022.](h
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摘要：[TOC] > [Wu L., Sun P., Fu Y., Hong R., Wang X. and Wang M. A neural influence diffusion model for social recommendation. SIGIR, 2019.](https://dl.acm
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摘要：[TOC] > [Liao J., Zhou W., Luo F., Wen J., Gao M., Li X. and Zeng J. SocialLGN: Light graph convolution network for social recommendation. Information
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摘要：[TOC] > [Liu H., Wei Y., Yin J. and Nie L. HS-GCN: Hamming spatial graph convolutional networks for recommendation. IEEE TKDE.](https://arxiv.org/pdf/
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摘要：[TOC] > [Trivedi H., Balasubramanian N., Khot T., Sabharwal A. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-st
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摘要：[TOC] > [He H., Zhang H. and Roth D. Rethinking with retrieval: faithful large language model inference. arXiv preprint arXiv:2301.00303, 2023.](http:
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摘要：[TOC] > [Guu K., Lee K., Tung Z., Pasupat P. and Chang M. REALM: Retrieval-augmented language model pre-training. ICML, 2020.](http://arxiv.org/abs/20
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摘要：[TOC] > [Lewis P. and Perez E., et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. NIPS, 2020.](http://arxiv.org/abs/2005.11401)
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摘要：[TOC] > [Cho S., Park E. and Yoo S. MEANTIME: Mixture of attention mechanisms with multi-temporal embeddings for sequential recommendation. RecSys, 20
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摘要：[TOC] > [Ma C., Ma L., Zhang Y., Sun J., Liu X. and Coates M. Memory augmented graph neural networks for sequential recommendation. AAAI, 2021.](http:
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