RSPapers | 工業界推薦系統論文合集

嘿，記得給“機器學習與推薦算法”添加星标

随着大資料時代的飛速發展，資訊逐漸呈現出過載狀态。推薦系統，作為近年來實作資訊生産者與消費者之間利益均衡化的有效手段之一，越來越發揮着舉足輕重的作用。像今日頭條、抖音這樣的APP之是以如此之火，讓人們欲罷不能，無非是抓住了使用者想看什麼的心理，那麼如何才能抓住使用者的心理，那就需要推薦系統的幫助了。是以在這個張揚個性的時代，無論你是開發工程師還是産品經理，我們都有必要了解一下個性化推薦的一些經典工作與前沿動态。于是我們于2018年建立了Github項目RSPapers：

https://github.com/hongleizhang/RSPapers

目前該項目已經有7位小夥伴加入參與貢獻，在此表示感謝。另外，如果大家看到比較好的論文，也歡迎送出。

該項目提供了推薦系統領域14大類研究方向，包括一些關于推薦系統的經典綜述文章、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論文（包括目前較火的GCN網絡）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子產品。目前累計Star數量已達2.8k，感謝大家的貢獻與支援。

由于推薦系統巨大的商業價值，一直以來都是學術界與工業界的研究熱點，尤其受到網際網路公司的熱捧，阿裡、京東、騰訊、谷歌、微軟等知名大廠都在推薦系統上做了大量的研究工作，并提出了一系列卓有成效的模型與算法。為了縮國小術界與工業界之間的鴻溝，把握工業界推薦系統的研究動向，了解工業界的經典工作與近期研究熱點，我們在RSPapers的基礎上新增了工業界推薦系統論文的彙總，收錄了工業界關于推薦系統的經典論文與近期的研究工作。工業界的推薦系統研究比較關注點選率預測、社會化推薦，以及盡可能充分利用商業平台上使用者産生的資訊來提高模型效果，同時也在強化學習推薦系統、圖神經網絡推薦系統等新興方向做了諸多探索。

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.

Facebook

• 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.

Google

• 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

推薦閱讀

RSPapers | 對話推薦系統論文合集

一文盡覽推薦系統模型演變史

當推薦系統邂逅深度學習

喜歡的話點個在看吧????