Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation
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Yongfeng Zhang | Joemon M. Jose | Xin Xin | Xiangnan He | Yongdong Zhang | Xiangnan He | Xin Xin | Yongdong Zhang | Yongfeng Zhang | J. Jose
[1] Xiangnan He,et al. Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention , 2017, SIGIR.
[2] Jianfeng Gao,et al. Embedding Entities and Relations for Learning and Inference in Knowledge Bases , 2014, ICLR.
[3] Martin Ester,et al. Collaborative Denoising Auto-Encoders for Top-N Recommender Systems , 2016, WSDM.
[4] Yixin Cao,et al. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences , 2019, WWW.
[5] George Karypis,et al. FISM: factored item similarity models for top-N recommender systems , 2013, KDD.
[6] S. C. Hui,et al. Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking , 2017, WWW.
[7] Greg Linden,et al. Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .
[8] Philip S. Yu,et al. Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model , 2018, KDD.
[9] Yixin Cao,et al. Explainable Reasoning over Knowledge Graphs for Recommendation , 2018, AAAI.
[10] Evangelia Christakopoulou,et al. Local Latent Space Models for Top-N Recommendation , 2018, KDD.
[11] Xu Chen,et al. Learning over Knowledge-Base Embeddings for Recommendation , 2018, Algorithms.
[12] Alex Graves,et al. Recurrent Models of Visual Attention , 2014, NIPS.
[13] Julian J. McAuley,et al. Translation-based Recommendation , 2017, RecSys.
[14] Minyi Guo,et al. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems , 2018, CIKM.
[15] Xiangnan He,et al. NAIS: Neural Attentive Item Similarity Model for Recommendation , 2018, IEEE Transactions on Knowledge and Data Engineering.
[16] Xiaoyu Du,et al. Adversarial Personalized Ranking for Recommendation , 2018, SIGIR.
[17] F. Maxwell Harper,et al. The MovieLens Datasets: History and Context , 2016, TIIS.
[18] Gediminas Adomavicius,et al. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.
[19] Steffen Rendle,et al. Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.
[20] Alessandro Bozzon,et al. Recurrent knowledge graph embedding for effective recommendation , 2018, RecSys.
[21] Lars Schmidt-Thieme,et al. BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.
[22] George Karypis,et al. User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items , 2015, ACM Trans. Intell. Syst. Technol..
[23] David A. McAllester,et al. Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence , 2009, UAI 2009.
[24] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[25] Tat-Seng Chua,et al. Neural Factorization Machines for Sparse Predictive Analytics , 2017, SIGIR.
[26] Kai Liu,et al. Deep Item-based Collaborative Filtering for Top-N Recommendation , 2018, ACM Trans. Inf. Syst..
[27] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[28] Minyi Guo,et al. DKN: Deep Knowledge-Aware Network for News Recommendation , 2018, WWW.
[29] Julian J. McAuley,et al. Self-Attentive Sequential Recommendation , 2018, 2018 IEEE International Conference on Data Mining (ICDM).
[30] Jure Leskovec,et al. Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time , 2017, WWW.
[31] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[32] Greg Linden,et al. Two Decades of Recommender Systems at Amazon.com , 2017, IEEE Internet Computing.
[33] Jason Weston,et al. Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.
[34] Zhiyuan Liu,et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion , 2015, AAAI.
[35] Edward Y. Chang,et al. Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks , 2018, SIGIR.
[36] Patrick Seemann,et al. Matrix Factorization Techniques for Recommender Systems , 2014 .
[37] John Riedl,et al. Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.
[38] Yehuda Koren,et al. Matrix Factorization Techniques for Recommender Systems , 2009, Computer.
[39] Jaana Kekäläinen,et al. Cumulated gain-based evaluation of IR techniques , 2002, TOIS.
[40] Nicholas Jing Yuan,et al. Collaborative Knowledge Base Embedding for Recommender Systems , 2016, KDD.
[41] Tat-Seng Chua,et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks , 2017, IJCAI.
[42] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[43] Hwanjo Yu,et al. Do "Also-Viewed" Products Help User Rating Prediction? , 2017, WWW.
[44] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[45] Paul Covington,et al. Deep Neural Networks for YouTube Recommendations , 2016, RecSys.
[46] M. B. Blake,et al. Two Decades of Recommender Systems at Amazon.com , 2017 .
[47] Dong Wang,et al. FHSM: Factored Hybrid Similarity Methods for Top-N Recommender Systems , 2016, APWeb.
[48] Tat-Seng Chua,et al. Neural Graph Collaborative Filtering , 2019, SIGIR.
[49] Mengting Wan,et al. Recommendation Through Mixtures of Heterogeneous Item Relationships , 2018, CIKM.
[50] Yoram Singer,et al. Local Low-Rank Matrix Approximation , 2013, ICML.