随笔分类 - Recommender Systems
摘要：Carbonell J. and Goldstein. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In International ACM SIGIR Con
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摘要：Zhang M. and Chen Y. Inductive matrix completion based on graph neural networks. In International Conference on Learning Representations (ICLR) 概 本文介绍
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摘要：Cai X., Huang C., Xia L. and Ren X. LightGCL: Simple yet effective graph contrastive learning for recommendation. In International Conference on Learn
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摘要：[TOC] > [Shi W., Chen J., Feng F., Zhang J., Wu J., Gao C. and He X. On the theories behind hard negative sampling for recommendation. In Internationa
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摘要：Chen J., Lian D., Jin B., Zhang K. and Chen E. Learning recommenders for implicit feedback with importance resampling. In ACM Web Conference (WWW), 20
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摘要：Kang W. and McAuley J. Self-attentive sequential recommendation. In IEEE International Conference on Data Mining (ICDM), 2018. 概 Transformer 最初用在序列推荐之
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摘要：Lian D., Liu Q. and Chen E. Personalized ranking with importance sampling. In International World Wide Web Conference (WWW), 2020. 概 作者总结了 4 种基于 impor
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摘要：Zhang W., Chen T., Wang J. and Yu Y. Optimizing top-n collaborative filtering via dynamic negative item sampling. In International ACM SIGIR Conferenc
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摘要：Sun F., Liu J., Wu J., Pei C., Lin X., Ou W. and Jiang P. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transfor
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摘要：Wu S., Tang Y., Zhu Y., Wang L., Xie X. and Tan T. Session-based recommendation with graph neural networks. In AAAI Conference on Advancement of Artif
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摘要：Hidasi B., Karatzoglou A., Baltrunas L. and Tikk D. Session-based recommendations with recurrent neural networks. In International Conference on Learn
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摘要：Yu F., Liu Q., Wu S., Wang L. and Tan T. A dynamic recurrent model for next basket recommendation. International ACM SIGIR Conference on Research and
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摘要：Zhang J., Chow C. and Li Y. In ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATITAL), 2014. 概 结合 social
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摘要：Choi J., Hong S., Park N. and Cho S. Perturbation-recovery method for recommendation. arXiv preprint arXiv:2211.09324, 2022. 概 本文将最近很火的 diffusion mode
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摘要：Choi J., Jeon J. and Park N. LT-OCF: Learnable-time ode-based collaborative filtering. In International Conference on Information and Knowledge Manage
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摘要：Wang Y., Zhao Y., Zhang Y. and Derr T. Collaboration-aware graph convolutional network for recommender systems. arXiv preprint arXiv:2207.06221, 2022.
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摘要：Wu L., Yang Y., Zhang K., Hong R., Fu Y. and Wang M. Joint item recommendation and attribute inference: an adaptive graph convolutional network approa
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摘要：Liu Z., Meng L., Jiang F., Zhang J. and Yu P. S. Deoscillated graph collaborative filtering. arXiv preprint arXiv:2011.02100, 2020. 概 作者认为鉴于推荐数据集二部图的特
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摘要：Ying R., He R., Chen K., Eksombatchai P., Hamilton W. L. and Leskovec J. Graph convolutional neural networks for web-scale recommender systems. In ACM
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摘要：Ren Y., Tang H. and Zhu S. Unbiased learning to rank with biased continuous feedback. In International Conference on Information and Knowledge Managem
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