随笔分类 - Recommender Systems
摘要：目录概Search-based Interest Model (SIM)子序列抽取Exact Search Unit Qi P., Zhu X., Zhou G., Zhang Y., Wang Z., Ren L., Fan Y. and Gai K. Search-based user inte
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摘要：目录概符号说明Popularity bias\(\mathbf{q}_1\) 和 \(\mathbf{r}\) 具有高相似度相似度随着维度降低而增加相似度随着训练的变化ReSN: Regulartion with Spectral Norm Lin S., Gao C., Chen J., Zhou
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摘要：目录概符号说明STARRetrievalRanking最后的结果 Lee D., Kraft A., Jin L., Mehta N., Xu T., Hong L., Chi E. H. and Yi X. STAR: A simple training-free approach for rec
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摘要：目录概Graph CLSimGCL代码 Yu J., Yin H., Xia X., Chen T., Cui L. and Huang N. Q. V. Are graph augmentations necessary? simple graph contrastive learning for
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摘要：目录概Decoupled Attention and Representation Embeddings (DARE) model Feng N., Pang J., Wu J., Chen B., Wang X., Li Q., Hu X., Jiang J. and Long M. Long-s
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摘要：目录概DCN-v2 Wang R., Shivanna R., Cheng D. Z., Jain S., Lin D., Hong L. and Chi E. D. DCN V2: Improved deep & cross network and practical lessons for we
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摘要：目录概符号说明BSARec (Beyond Self-Attention for Sequential Recommendation)代码 Shin Y., Choi J., Wi H. and Park N. An attentive inductive bias for sequential r
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摘要：目录概RecAgentProfile moduleMemory moduleAction module Wang L, Zhang J., Yang H., Chen Z., Tang J., Zhang Z., Chen X., Lin Y., Sun H., Song R., Zhao W. X
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摘要：目录概CSHI (Controllable, Scalable, and Human-Involved)代码 Zhu L., Huang X. and Sang J. A llm-based controllable, scalable, human-involved user simulator
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摘要：目录概符号说明UniSRec统一的文本表示统一的序列表示Parameter-Efficient Fine-tuning代码 Hou Y., Mu S., Zhao W. X., Li Y., Ding B. and Wen J. Towards Universal Sequence Represen
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摘要：目录概主要内容 Deng X., Xu L., Li X., Yu J., Xue E., Wang Z., Zhang D., Liu Z., Song Y., Zhou G., Mou N. and Jiang S. End-to-end training of multimodal model
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摘要：目录概符号说明MotivationLGMRecLocal Graph EmbeddingGlobal Graph EmbeddingFusion代码 Guo Z., Li J., Li G., Wang C., Shi S. and Ruan B. LGMRec: Local and global
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摘要：目录概GRs (Generative Recommenders)任务形式模型设计代码 Zhai J., Liao L., Liu X., Wang Y., Li R., Cao X., Gao L., Gong Z., Gu F., He M., Lu Y. and Shi Y. Actions s
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摘要：目录概BLaIR代码 Hou Y., Li J., He Z., Yan A., Chen X., and McAuley J. Bridging language and items for retrieval and recommendation. 2024. 概 本文提出了一种利用对比损失训练
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摘要：目录概符号说明MotivationMulti-Task Pairwise Ranking (MTPR)代码 Du X., Wang X., He X., Li Z., Tang J. and Chua T. How to learn item representation for cold-star
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摘要：目录概符号说明MotivationKnowledge Distillation framework for modality-enriched Sequential Recommenders (KGSR)相似度建模相似度预测训练 Hu H., Liu Q., Li C. and Kan M. Lig
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摘要：目录概实验设置Evaluation MetricsMetric 的一致性不同的 metrics 导致的算法排名差异Sampled metricsSampled metrics 是否会导致和 full ranking 的 metrics 不同的评价数据集构建数据集的选择和预处理\(k\)-core f
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摘要：目录概主要内容数据集统计信息Top-N Recommendation ListRecommendation Accuracy理想的切分方式代码 Ji Y., Sun A., Zhang J. and Li C. A critical study on data leakage in recommen
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摘要：目录概实验设置实验Interaction-based LoyaltyActive Time Period based LoyaltyRecency代码 Ji Y., Sun A., Zhang J. and Li C. Do Loyal Users Enjoy Better Recommendati
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摘要：目录概MotivationAlterRec代码 Li J., Han H., Chen Z., Shomer H., Jin W., Javari A. and Tang J. Enhancing ID and text fusion via alternative training in sess
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