RSPapers | 工业界推荐系统论文合集

嘿，记得给“机器学习与推荐算法”添加星标

随着大数据时代的飞速发展，信息逐渐呈现出过载状态。推荐系统，作为近年来实现信息生产者与消费者之间利益均衡化的有效手段之一，越来越发挥着举足轻重的作用。像今日头条、抖音这样的APP之所以如此之火，让人们欲罢不能，无非是抓住了用户想看什么的心理，那么如何才能抓住用户的心理，那就需要推荐系统的帮助了。因此在这个张扬个性的时代，无论你是开发工程师还是产品经理，我们都有必要了解一下个性化推荐的一些经典工作与前沿动态。于是我们于2018年创建了Github项目RSPapers：

https://github.com/hongleizhang/RSPapers

目前该项目已经有7位小伙伴加入参与贡献，在此表示感谢。另外，如果大家看到比较好的论文，也欢迎提交。

该项目提供了推荐系统领域14大类研究方向，包括一些关于推荐系统的经典综述文章、主流的推荐算法文章、社会化推荐算法论文、基于深度学习的推荐系统论文（包括目前较火的GCN网络）以及关于专门处理冷启动问题的相关论文、推荐中的效率问题以及推荐当中的探索与利用问题、推荐可解释性、基于评论的推荐等。当然该项目包含但不局限于以上这些模块。目前累计Star数量已达2.8k，感谢大家的贡献与支持。

由于推荐系统巨大的商业价值，一直以来都是学术界与工业界的研究热点，尤其受到互联网公司的热捧，阿里、京东、腾讯、谷歌、微软等知名大厂都在推荐系统上做了大量的研究工作，并提出了一系列卓有成效的模型与算法。为了缩小学术界与工业界之间的鸿沟，把握工业界推荐系统的研究动向，了解工业界的经典工作与近期研究热点，我们在RSPapers的基础上新增了工业界推荐系统论文的汇总，收录了工业界关于推荐系统的经典论文与近期的研究工作。工业界的推荐系统研究比较关注点击率预测、社会化推荐，以及尽可能充分利用商业平台上用户产生的信息来提高模型效果，同时也在强化学习推荐系统、图神经网络推荐系统等新兴方向做了诸多探索。

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

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