最新最全推荐系统相关优秀研究论文整理分享

    推荐系统是利用电子商务网站向客户提供商品信息和建议，帮助用户决定应该购买什么产品，模拟销售人员帮助客户完成购买过程。个性化推荐是根据用户的兴趣特点和购买行为，向用户推荐用户感兴趣的信息和商品。

    随着电子商务规模的不断扩大，商品个数和种类快速增长，顾客需要花费大量的时间才能找到自己想买的商品。这种浏览大量无关的信息和产品过程无疑会使淹没在信息过载问题中的消费者不断流失。为了解决这些问题，个性化推荐系统应运而生。个性化推荐系统是建立在海量数据挖掘基础上的一种高级商务智能平台，以帮助电子商务网站为其顾客购物提供完全个性化的决策支持和信息服务。

    本资源整理了最近几年推荐系统相关的最新的一些，值得阅读的优质论文，涉及基于内容推荐算法、基于协同过滤推荐算法、可解释下推荐算法、序列推荐算法（Session-based推荐算法、上下文结合序列推荐）、基于知识图谱推荐算法、基于强化学习推荐算法、ctr预估、召回、多目标、多任务等推荐各个方面。

    资源整理自网络，源地址: https://github.com/mengfeizhang820/Paperlist-for-Recommender-Systems

 

研究论文

    •Deep Learning based Recommender System: A Survey and New Perspectives [2017]

    •基于深度学习的推荐系统研究综述 [2018] 

    •Explainable Recommendation: A Survey and New Perspectives [2018] 

    •Sequence-Aware Recommender Systems [2018] 

    •DeepRec: An Open-source Toolkit for Deep Learning based Recommendation [IJCAI 2019] 

 

基于内容推荐算法

    •Convolutional Matrix Factorization for Document Context-Aware Recommendation [RecSys 2016]  

    •Joint Deep Modeling of Users and Items Using Reviews for Recommendation [WSDM 2017]

    •Multi-Pointer Co-Attention Networks for Recommendation [KDD 2018]

    •Gated attentive-autoencoder for content-aware recommendation [WSDM 2019]

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广告

深度学习推荐系统（全彩）

作者：王喆

京东

基于协同过滤推荐算法

    •Neural Collaborative Filtering [WWW 2017]

    •Collaborative Denoising Auto-Encoders for Top-N Recommender Systems  

    •Outer Product-based Neural Collaborative Filtering [IJCAI 2018]

    •Neural Graph Collaborative Filtering [SIGIR 2019] 

    •Transnets: Learning to transform for recommendation [RecSys 2017]

    •Metric Factorization: Recommendation beyond Matrix Factorization 

    •Improving Top-K Recommendation via Joint Collaborative Autoencoders 

    •Collaborative Metric Learning [WWW2017]

    •NeuRec : On Nonlinear Transformation for Personalized Ranking [IJACA 2018] 

    •DeepCF : A Unified Framework of Representation Learning and Matching Function Learning in Recommender System [AAAI2019 oral] 

    •Graph neural networks for social recommendation [WWW2019] 

    •STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems [[IJCAI2019]]  

    •Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks [[ICTIR2019]]  

    •Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation [AAAI2020] 

 

可解释推荐算法

    •Explainable Recommendation via Multi-Task Learning in Opinionated Text Data [SIGIR 2018]

    •TEM: Tree-enhanced Embedding Model for Explainable Recommendation [WWW 2018]

    •Neural Attentional Rating Regression with Review-level Explanations [WWW 2018] 

 

 

序列推荐算法

    Session-based推荐算法

    •Session-based Recommendations with Recurrent Neural Networks [ICLR 2016] 

    •Neural Attentive Session-based Recommendation [CIKM 2017] 

    •When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation [RecSys 2017]

    •STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation [KDD 2018] 

    •RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation [AAAI 2019]

    •Session-based Recommendation with Graph Neural Networks [AAAI 2019]

    •Streaming Session-based Recommendation [KDD 2019] 

    •Session-based Social Recommendation via Dynamic Graph Attention Networks [WSDM 2019]

    •Sequence and Time Aware Neighborhood for Session-based Recommendations [SIGIR 2019] 

    •Performance Comparison of Neural and Non-Neural Approaches to Session-based Recommendation [RecSys 2019]

    •Predictability Limits in Session-based Next Item Recommendation [RecSys 2019]

    •Empirical Analysis of Session-Based Recommendation Algorithms [2019] 

    •A Collaborative Session-based Recommendation Approach with Parallel Memory Modules [SIGIR2019] 

    •Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks [CIKM2019]

    •Session-based Recommendation with Hierarchical Memory Networks [CIKM2019] 

    •ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation [IJCAI2019]

    •Variational Session-based Recommendation Using Normalizing Flows [WWW2019] 

 

    Last-N方法

    •Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding [WSDM 2018]

    •Self-Attentive Sequential Recommendation [ICDM 2018] 

    •Hierarchical Gating Networks for Sequential Recommendation [KDD 2019]

    •Next Item Recommendation with Self-Attention [ACM 2018]

 

    Long and short-term序列推荐

    •Collaborative Memory Network for Recommendation Systems [SIGIR 2018]

    •Sequential Recommender System based on Hierarchical Attention Network [IJCAI 2018] 

    •Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems [WWW 2019] 

    •A Large-scale Sequential Deep Matching Model for E-commerce Recommendation[CIKM 2019]

    •Recurrent Neural Networks for Long and Short-Term Sequential Recommendation [RecSys 2018] 

    •A Dynamic Co-attention Network for Session-based Recommendation [CIKM 2019]

    •Personalizing Graph Neural Networks with Attention Mechanism for Session-based Recommendation [TKDE 2019] 

    •A Long-Short Demands-Aware Model for Next-Item Recommendation [CoRR 2019]

    •Learning from History and Present : Next-item Recommendation via Discriminatively Exploiting User Behaviors [KDD 2018][JD]

    •Towards Neural Mixture Recommender for Long Range Dependent User Sequences[WWW 2019]

    •A Review-Driven Neural Model for Sequential Recommendation [IJCAI 2019] 

    •Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation [IJCAI 2019] [Microsoft]

    •Long- and Short-term Preference Learning for Next POI Recommendation [CIKM 2019] 

    •Neural News Recommendation with Long- and Short-term User Representations [ACL 2019][Microsoft]

 

    上下文结合序列推荐

    •Context-Aware Sequential Recommendations withStacked Recurrent Neural Networks [WWW 2019]

 

    其他

    •Hierarchical Neural Variational Model for Personalized Sequential Recommendation [WWW 2019]

    •Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics [KDD 2019]

    •Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit [KDD 2019]

    •Taxonomy-aware Multi-hop Reasoning Networks for Sequential Recommendation [WSDM 2019]

    •Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling [CIKM 2019] 

 

基于知识图谱推荐算法

    •Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks [SIGIR 2018] 

    odataset and code : https://github.com/RUCDM/KB4Rec

    •DKN: Deep Knowledge-Aware Network for News Recommendation [WWW 2018] 

    •RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems [CIKM 2018] 

    •Knowledge Graph Convolutional Networks for Recommender Systems [WWW 2019] 

    •KGAT: Knowledge Graph Attention Network for Recommendation [KDD2019]

 

基于强化学习推荐算法

    •DRN: A Deep Reinforcement Learning Framework for News Recommendation [WWW 2018] 

    •Mention Recommendation in Twitter with Cooperative Multi-Agent Reinforcement Learning [SIGIR 2019]

    •Reinforcement Learning for User Intent Prediction in Customer Service Bots [SIGIR2019]

    •Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems [KDD2019]

    •Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology [IJCAI 2019]  [Youtube]

    •Top-K Off-Policy Correction for a REINFORCE Recommender System [WSDM 2019] [[Youtube]]

 

Multi-behavior学习推荐

    •Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation [AAAI2020]

 

多任务学习推荐算法

    •Entire Space Multi-Task Model: An E ective Approach for Estimating Post-Click Conversion Rate [SIGIR2018]

    •Conversion Rate Prediction via Post-Click Behaviour Modeling

    •Rerceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks [KDD2018]

    •Modeling task relationships in multi-task learning with multi-gate mixture-of-experts [KDD2018]

    •Recommending What Video to Watch Next: A Multitask Ranking System [RecSys2019]

 

重排序算法

    •Personalized Re-ranking for Recommendation [RecSys2019][dataset]

    •Learning a Deep Listwise Context Model for Ranking Refinement [SIGIR2018]

 

工业级

    CTR预估

    •DeepFM: A Factorization-Machine based Neural Network for CTR Prediction [[IJCAI 2017]  [Huawei]

    •xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems] [KDD2018]  [Microsoft]

    •Order-aware Embedding Neural Network for CTR Prediction][SIGIR 2019]  [Huawei]

    •Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction [WWW 2019]  [Huawei]

    •Interaction-aware Factorization Machines for Recommender Systems [AAAI2019] [Tencent]

 

    召回

    •[Embedding] Item2Vec-Neural Item Embedding for Collaborative Filtering [Microsoft 2017]

    •[Embedding] DeepWalk- Online Learning of Social Representations [KDD 2014]

    •[Embedding] LINE - Large-scale Information Network Embedding [Microsoft 2015]

    •[Embedding] Node2vec - Scalable Feature Learning for Networks [Stanford 2016]

    •[Embedding] Structural Deep Network Embedding [KDD2016] 

    •[Embedding] Item2Vec-Neural Item Embedding for Collaborative Filtering [Microsoft 2017]

    •[Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb [KDD 2018] 

    •[Embedding] Graph Convolutional Neural Networks for Web-Scale Recommender Systems [KDD 2018] [Pinterest]

    •Is a Single Embedding Enough ? Learning Node Representations that Capture Multiple Social Contexts [WWW 2019] 

    •[Embedding] Representation Learning for Attributed Multiplex Heterogeneous Network [KDD 2019] 

    •[DNN Match] Deep Neural Networks for YouTube Recommendations [RecSys 2016] [Youtube]

    •[DNN Match] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations[RecSys 2019] 

    •[Semantic Match] Deep Semantic Matching for Amazon Product Search [WSDM 2019][Amazon]

    •[Tree Match] Joint Optimization of Tree-based Index and Deep Model for Recommender Systems [NeurIPS 2019] [Tencent]

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广告

深度学习推荐系统（全彩）

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京东

    其他

    •Latent Cross: Making Use of Context in Recurrent Recommender Systems [WSDM 2018][Youtube]

    •Learning from History and Present: Next-item Recommendation via Discriminatively Exploting Users Behaviors [KDD 2018]

    •Real-time Attention Based Look-alike Model for Recommender System [KDD 2019]  [Tencent]

 

 Alibaba相关论文

    •[Match] TDM：Learning Tree-based Deep Model for Recommender Systems [KDD2018] 

    •[Match] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall [2019]

    •[Long and short-term] SDM: Sequential Deep Matching Model for Online Large-scale Recommender System [CIKM 2019]

    •[Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba [KDD 2018]

    •[Embedding] Learning and Transferring IDs Representation in E-commerce [KDD 2018] 

    •[Representations] ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation [AAAI 2018] 

    •[Representations] Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks [KDD2018]

    •[exact-K recommendation] Exact-K Recommendation via Maximal Clique Optimization [KDD 2019]

    •[Explain]A Capsule Network for Recommendation and Explaining What You Like and Dislike [SIGIR2019]

    •[CTR] Privileged Features Distillation for E-Commerce Recommendations [Woodstock ’18]

    •[CTR] Representation Learning-Assisted Click-Through Rate Prediction [IJCAI 2019] 

    •[CTR] Deep Session Interest Network for Click-Through Rate Prediction [IJCAI 2019] 

    •[CTR] Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction] [KDD2019]  

    •[CTR] Graph Intention Network for Click-through Rate Prediction in Sponsored Search [SIGIR2019] 

    •[CTR] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction [MLR]

    •[CTR] Deep Interest Evolution Network for Click-Through Rate Prediction [AAAI2019]

    •[CTR] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction[KDD2019] 

    •[CTR] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba 

    •[CVR] Entire Space Multi-Task Model: An E ective Approach for Estimating Post-Click Conversion Rate [SIGIR2018]