【學術相關】RSPapers | 工業(yè)界推薦系統(tǒng)論文合集
隨著大數(shù)據(jù)時代的飛速發(fā)展，信息逐漸呈現(xiàn)出過載狀態(tài)。推薦系統(tǒng)，作為近年來實現(xiàn)信息生產者與消費者之間利益均衡化的有效手段之一，越來越發(fā)揮著舉足輕重的作用。像今日頭條、抖音這樣的APP之所以如此之火，讓人們欲罷不能，無非是抓住了用戶想看什么的心理，那么如何才能抓住用戶的心理，那就需要推薦系統(tǒng)的幫助了。因此在這個張揚個性的時代，無論你是開發(fā)工程師還是產品經(jīng)理，我們都有必要了解一下個性化推薦的一些經(jīng)典工作與前沿動態(tài)。于是我們于2018年創(chuàng)建了Github項目RSPapers：
https://github.com/hongleizhang/RSPapers
該項目提供了推薦系統(tǒng)領域14大類研究方向，包括一些關于推薦系統(tǒng)的經(jīng)典綜述文章、主流的推薦算法文章、社會化推薦算法論文、基于深度學習的推薦系統(tǒng)論文（包括目前較火的GCN網(wǎng)絡）以及關于專門處理冷啟動問題的相關論文、推薦中的效率問題以及推薦當中的探索與利用問題、推薦可解釋性、基于評論的推薦等。當然該項目包含但不局限于以上這些模塊。目前累計Star數(shù)量已達2.8k，感謝大家的貢獻與支持。
Industrial RS
Airbnb
Mihajlo et al.?Real-time Personalization using Embeddings for Search Ranking at Airbnb.?KDD.2018.
Alibaba
Kun et al.?Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction.?arXiv, 2017.
Zhibo et al.?Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba.?KDD, 2018.
Guorui et al.?Deep Interest Evolution Network for Click-Through Rate Prediction.?AAAI, 2019.
Guorui et al.?Deep Interest Network for Click-Through Rate Prediction.?KDD, 2018.
Xiao et al.?Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate.?SIGIR, 2018.
Han et al.?Learning Tree-based Deep Model for Recommender Systems.?KDD, 2018.
Lin et al.?Visualizing and Understanding Deep Neural Networks in CTR Prediction.?SIGIR, 2018.
Qiwei et al.?Behavior Sequence Transformer for E-commerce Recommendation in Alibaba.?KDD,2019.
Wentao et al.?Click-Through Rate Prediction with the User Memory Network.?KDD, 2019
Yufei et al.?Deep Session Interest Network for Click-Through Rate Prediction.?arXiv, 2019.
Wentao et al.?Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction.?KDD, 2019.
Han et al.?Joint Optimization of Tree-based Index and Deep Model for Recommender Systems.?NIPS, 2019.
Chao et al.?Multi-Interest Network with Dynamic Routing for Recommendation at Tmall.?CIKM, 2019.
Qi et al.?Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction.?KDD, 2019.
Wentao et al.?Representation Learning-Assisted Click-Through Rate Prediction.?arXiv, 2019.
Fuyu et al.?SDM: Sequential Deep Matching Model for Online Large-scale Recommender System.?CIKM, 2019.
Wentao et al.?MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction.?CIKM, 2020.
Zhe et al.?COLD: Towards the Next Generation of Pre-Ranking System.?KDD, 2020.
Weinan et al.?Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction.?SIGIR, 2020.
Ze et al.?Deep Match to Rank Model for Personalized Click-Through Rate Prediction.?AAAI, 2020.
Shu-Ting et al.?Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution.?AAAI, 2020.
Changhua et al.?Personalized Re-ranking for Recommendation.?RecSys, 2019.
Liyi et al.?A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction.?CIKM, 2020.
Yu et al.?EdgeRec: Recommender System on Edge in Mobile Taobao.?CIKM, 2020.
Yufei et al.?MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction.?CIKM, 2020.
Baidu
Xiangyu et al.?Whole-Chain Recommendations.?CIKM, 2020.
Criteo
Yuchin et al.?Field-aware Factorization Machines for CTR Prediction.?RecSys, 2016.
Xinran et al.?Practical Lessons from Predicting Clicks on Ads at Facebook.?KDD, 2014.
Maxim et al.?Deep Learning Recommendation Model for Personalization and Recommendation Systems.?arXiv, 2019.
James et al.?The YouTube Video Recommendation System.?RecSys, 2010.
Jason et al.?Label Partitioning For Sublinear Ranking.?JMLR, 2013.
Paul et al. Deep Neural Networks for YouTube Recommendations.** RecSys, 2016.
Heng-Tze et al.?Wide & Deep Learning for Recommender Systems.?DLRS, 2016.
Ruoxi et al.?Deep & Cross Network for Ad Click Predictions.?KDD, 2017.
Alex et al.?Latent Cross: Making Use of Context in Recurrent Recommender Systems.?WSDM, 2018.
Alex et al.?Fairness in Recommendation Ranking through Pairwise Comparisons.?KDD, 2019.
Xinyang et al.?Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations.?RecSys, 2019.
Huawei
Huifeng et al.?DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.?IJCAI, 2017.
Bin et al.?Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction.?WWW, 2019.
Huifeng et al.?PAL: A Position-bias Aware Learning Framework for CTR Prediction in Live Recommender Systems.?RecSys, 2019.
Kai et al.?Automatic Feature Engineering From Very High Dimensional Event Logs Using Deep Neural Networks.?KDD, 2019.
Yishi et al.?GraphSAIL Graph Structure Aware Incremental Learning for Recommender Systems.?CIKM, 2020.
JingDong
Huifeng et al.?DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction.?arXiv, 2018.
Meizi et al.?Micro Behaviors: A New Perspective in E-commerce Recommender Systems.?WSDM, 2018.
Xiangyu et al.?Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning.?KDD, 2018.
Xiangyu et al.?Deep Reinforcement Learning for List-wise Recommendations.?arXiv, 2019.
Wenqi et al.?Deep Social Collaborative Filtering.?RecSys, 2019.
Wenqi et al.?Graph Neural Networks for Social Recommendation.?WWW, 2019.
Meituan
Hongwei et al.?Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems.?KDD, 2019.
Microsoft
Po-Sen et al.?Learning Deep Structured Semantic Models for Web Search using Clickthrough Data.?CIKM, 2013.
Ali Elkahky et al.?A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems.?WWW, 2015.
Oren et al.?ITEM2VEC: NEURAL ITEM EMBEDDING FOR COLLABORATIVE FILTERING.?ICML, 2016.
Hongwei et al.?DKN: Deep Knowledge-Aware Network for News Recommendation.?WWW, 2018.
Guanjie et al.?DRN: A Deep Reinforcement Learning Framework for News Recommendation.?WWW, 2018.
John et al.?Modeling and Simultaneously Removing Bias via Adversarial Neural Networks.?arXiv, 2018.
Chen et al.?Privileged Features Distillation at Taobao Recommendations.?KDD, 2020.
Hongwei et al.?RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems.?CIKM, 2018.
Jianxun et al.?xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems.?KDD, 2018.
Zhongxia et al.?Co-Attentive Multi-Task Learning for Explainable Recommendation.?IJCAI, 2019.
Chuhan et al.?Neural News Recommendation with Attentive Multi-View Learning.?IJCAI, 2019.
Le et al.?Personalized Multimedia Item and Key Frame Recommendation.?IJCAI, 2019.
Fuyu et al.?SDM: Sequential Deep Matching Model for Online Large-scale Recommender System.?CIKM, 2019.
Shu et al.?Session-Based Recommendation with Graph Neural Networks.?AAAI, 2019.
Le et al.?SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation.?arXiv, 2019.
Netflix
Balazs et al.?Session-based recommendations with recurrent neural networks.?ICLR, 2016.
Sina
Junlin et al.?FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine.?arXiv, 2019.
Tencent
Qitian et al.?Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems.?WWW, 2019.
Wen et al.?Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction.?WWW, 2020.
Ruobing et al.?Deep Feedback Network for Recommendation.?IJCAI, 2020
Tongwen et al.?GateNet:Gating-Enhanced Deep Network for Click-Through Rate Prediction.?arXiv, 2020.
Yahoo
Junwei et al.?Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising.?WWW, 2018.
Shaunak et al.?Learning to Create Better Ads Generation and Ranking Approaches for Ad Creative Refinement.?CIKM, 2020
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