暂无分享,去创建一个
Chun Chen | Xiangnan He | Martin Ester | Sheng Zhou | Jiawei Chen | Can Wang | Yan Feng | Chengquan Jiang | Xiangnan He | Chun Chen | Can Wang | Jiawei Chen | Yan Feng | Sheng Zhou | M. Ester | Chengquan Jiang | Chengquan Jiang
[1] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[2] Diane Kelly,et al. IMPLICIT FEEDBACK: USING BEHAVIOR TO INFER RELEVANCE , 2005 .
[3] Jun Wang,et al. Optimizing top-n collaborative filtering via dynamic negative item sampling , 2013, SIGIR.
[4] Tong Zhao,et al. Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering , 2014, CIKM.
[5] Yifan Hu,et al. Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[6] Xiangnan He,et al. Bias and Debias in Recommender System: A Survey and Future Directions , 2020, ACM Trans. Inf. Syst..
[7] Matthew D. Hoffman,et al. Variational Autoencoders for Collaborative Filtering , 2018, WWW.
[8] Kun Gai,et al. Learning Tree-based Deep Model for Recommender Systems , 2018, KDD.
[9] Tat-Seng Chua,et al. Neural Graph Collaborative Filtering , 2019, SIGIR.
[10] Qiang Yang,et al. One-Class Collaborative Filtering , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[11] Peng Zhang,et al. IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models , 2017, SIGIR.
[12] Christopher C. Johnson. Logistic Matrix Factorization for Implicit Feedback Data , 2014 .
[13] Xiangnan He,et al. A Generic Coordinate Descent Framework for Learning from Implicit Feedback , 2016, WWW.
[14] Tat-Seng Chua,et al. Neural Collaborative Filtering , 2017, WWW.
[15] Xiangnan He,et al. Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems , 2020, WSDM.
[16] Martin Ester,et al. TrustWalker: a random walk model for combining trust-based and item-based recommendation , 2009, KDD.
[17] Jeffery P. Bray,et al. Consumer Behaviour Theory: Approaches and Models , 2008 .
[18] Steffen Rendle,et al. Improving pairwise learning for item recommendation from implicit feedback , 2014, WSDM.
[19] Xiaoyu Du,et al. Outer Product-based Neural Collaborative Filtering , 2018, IJCAI.
[20] Chun Chen,et al. Modeling Users' Exposure with Social Knowledge Influence and Consumption Influence for Recommendation , 2018, CIKM.
[21] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[22] Depeng Jin,et al. An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data , 2018, WWW.
[23] Tat-Seng Chua,et al. Fast Matrix Factorization for Online Recommendation with Implicit Feedback , 2016, SIGIR.
[24] Reid A. Johnson,et al. Calibrating Probability with Undersampling for Unbalanced Classification , 2015, 2015 IEEE Symposium Series on Computational Intelligence.
[25] Depeng Jin,et al. Sampler Design for Bayesian Personalized Ranking by Leveraging View Data , 2018, IEEE Transactions on Knowledge and Data Engineering.
[26] Zi Huang,et al. Neural Memory Streaming Recommender Networks with Adversarial Training , 2018, KDD.
[27] Depeng Jin,et al. Reinforced Negative Sampling for Recommendation with Exposure Data , 2019, IJCAI.
[28] Jiliang Tang,et al. Deep Adversarial Social Recommendation , 2019, IJCAI.
[29] Yixin Cao,et al. Reinforced Negative Sampling over Knowledge Graph for Recommendation , 2020, WWW.
[30] Zoubin Ghahramani,et al. Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices , 2014, ICML.
[31] Weinan Zhang,et al. LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates , 2016, CIKM.
[32] Xiangliang Zhang,et al. WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation , 2018, AAAI.
[33] Xing Xie,et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems , 2018, KDD.
[34] Abraham Bernstein,et al. Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks , 2015, RecSys.
[35] Deshi Ye,et al. Adaptive Influence Blocking: Minimizing the Negative Spread by Observation-Based Policies , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).
[36] Heng Huang,et al. Self-Paced Network Embedding , 2018, KDD.
[37] Yan Feng,et al. SamWalker: Social Recommendation with Informative Sampling Strategy , 2019, WWW.
[38] Chih-Jen Lin,et al. Selection of Negative Samples for One-class Matrix Factorization , 2017, SDM.
[39] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[40] Shujian Huang,et al. Deep Matrix Factorization Models for Recommender Systems , 2017, IJCAI.
[41] Paul Covington,et al. Deep Neural Networks for YouTube Recommendations , 2016, RecSys.
[42] Yuta Saito,et al. Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback , 2020, WSDM.
[43] Bamshad Mobasher,et al. Weighted Random Walk Sampling for Multi-Relational Recommendation , 2017, UMAP.
[44] Peng Cui,et al. On the Equivalence of Decoupled Graph Convolution Network and Label Propagation , 2021, WWW.
[45] Yongdong Zhang,et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation , 2020, SIGIR.
[46] Bo Song,et al. Neural Collaborative Ranking , 2018, CIKM.
[47] Dit-Yan Yeung,et al. Collaborative Deep Learning for Recommender Systems , 2014, KDD.
[48] Yi Chang,et al. Adversarial Sampling and Training for Semi-Supervised Information Retrieval , 2018, WWW.
[49] Martin Ester,et al. DGE: Deep Generative Network Embedding Based on Commonality and Individuality , 2020, AAAI.
[50] Jimmy J. Lin,et al. Fast candidate generation for two-phase document ranking: postings list intersection with bloom filters , 2012, CIKM.
[51] David A. McAllester,et al. Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence , 2009, UAI 2009.
[52] Jure Leskovec,et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems , 2018, KDD.
[53] Xin Wang,et al. Social Recommendation with Strong and Weak Ties , 2016, CIKM.
[54] Douglas W. Oard,et al. Implicit Feedback for Recommender Systems , 1998 .
[55] Lars Schmidt-Thieme,et al. BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.
[56] Bernhard Schölkopf,et al. Learning with Local and Global Consistency , 2003, NIPS.
[57] Xing Xie,et al. A Novel User Representation Paradigm for Making Personalized Candidate Retrieval , 2019, ArXiv.
[58] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[59] Weinan Zhang,et al. BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation , 2017, IUI.