随笔分类 - Recommender Systems
摘要：[TOC] > [Xu D., Ruan C., Kumar S., Korpeoglu E. and Achan K. Self-attention with functional time representation learning. NIPS, 2019.](http://arxiv.or
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摘要：[TOC] > [Ye Y., Xia L. and Huang C. Graph masked autoencoder for sequential recommendation. SIGIR, 2023.](http://arxiv.org/abs/2305.04619) ## 概 图 + MA
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摘要：[TOC] > [Li J., Wang Y., McAuley J. Time interval aware self-attention for sequential recommendation. WSDM, 2020.](https://dl.acm.org/doi/10.1145/3336
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摘要：[TOC] > [Tian Z., Bai T., Zhao W., Wen J. and Cao Z. Eulernet: Adaptive feature interaction learning via euler’s formula for ctr prediction. SIGIR, 20
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摘要：[TOC] > [Qiu R., Huang Z., Chen T. and Yin H. Exploiting positional information for session-based recommendation. ACM Transactions on Information Syst
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摘要：[TOC] > [Qiu R., Huang Z., Ying H. and Wang Z. Contrastive learning for representation degeneration problem in sequential recommendation. WSDM, 2022.]
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摘要：[TOC] > [Xia X., Yin H., Yu J., Wang Q., Cui L and Zhang X. Self-supervised hypergraph convolutional networks for session-based recommendation. AAAI,
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摘要：[TOC] > [Xia X., Yin H., Yu J., Shao Y. and Cui L. Self-supervised graph co-training for session-based recommendation. CIKM, 2021.](http://arxiv.org/a
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摘要：[TOC] > [Wang Z., Wei W., Cong G., Li X., Mao X. and Qiu M. Global context enhanced graph neural networks for session-based recommendation. SIGIR, 202
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摘要：[TOC] >[ Li J., Ren P., Chen Z., Ren Z., Lian T. and Ma J. Neural attentive session-based recommendation. CIKM, 2017.](http://arxiv.org/abs/1711.04725
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摘要：[TOC] > [Liu Q., Zeng Y., Mokhosi R. and Zhang H. STAMP: Short-term attention/memory priority model for session-based recommendation. KDD, 2018.](http
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摘要：Chen T. and Wong R. C. Handling information loss of graph neural networks for session-based recommendation. KDD, 2020. 概 作者发现图用在 Session 推荐中存在: lossy
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摘要：Li Z., Sun A. and Li C. DiffuRec: A diffusion model for sequential recommendation. arXiv preprint arXiv:2304.00686, 2023. 概 扩散模型用于序列推荐, 性能提升很大. DiffuR
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摘要：Fan Z., Liu Z., Wang A., Nazari Z., Zheng L., Peng H. and Yu P. S. Sequential recommendation via stochastic self-attention. International World Wide W
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摘要：Wang L. and Joachims T. Uncertainty quantification for fairness in two-stage recommender systems. In International World Wide Web Conference (WWW), 20
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摘要：Zhang Y., Dong X., Ding W., Li B., Jiang P. and Gai K. Divide and Conquer: Towards better embedding-based retrieval for recommender systems from a mul
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摘要：Wu H., Zhang Y., Ma C., Lyu F., Diaz F. and Liu X. A survey of diversification techniques in search and recommendation. arXiv preprint arXiv:2212.1446
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摘要：Clarke C. L. A., Kolla M., Cormack G. V., Vechtomova O., Ashkan A., B\ddot{u}ttcher S. and MacKinnon I. Novelty and diversity in information retrieval
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摘要：Zhai C., Cohen W. W. and Lafferty J. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In International ACM SIGIR C
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摘要：Wilhelm M., Ramanathan A., Bonomo A., Jain S., Chi E. H. and Gillenwater J. Practical diversified recommendations on youtube with determinantal point
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