推荐系统论文集锦

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【导读】本文整理了一系列推荐系统相关的论文

Github链接：

https://github.com/mengfeizhang820/Paperlist-for-Recommender-Systems

综述论文

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

    • https://arxiv.org/pdf/1707.07435.pdf

  • 基于深度学习的推荐系统研究综述 [2018]

    • http://cjc.ict.ac.cn/online/bfpub/hlww-2018124152810.pdf

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

    • https://arxiv.org/pdf/1804.11192.pdf

  • Sequence-Aware Recommender Systems [2018]

    • https://arxiv.org/pdf/1802.08452.pdf

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

    • https://arxiv.org/pdf/1905.10536.pdf

基于内容的推荐系统

Review-based Approaches

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

    • https://github.com/cartopy/ConvMF

  • Joint Deep Modeling of Users and Items Using Reviews for Recommendation

    • https://arxiv.org/pdf/1701.04783.pdf

    • https://github.com/chenchongthu/DeepCoNN

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

    • https://arxiv.org/pdf/1801.09251

    • https://github.com/vanzytay/KDD2018_MPCN

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

    • https://arxiv.org/pdf/1812.02869

    • https://github.com/allenjack/GATE

协同过滤推荐系统

  • Neural Collaborative Filtering

    • https://arxiv.org/pdf/1708.05031.pdf

    • https://paperswithcode.com/paper/neural-collaborative-filtering-1#code

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

    • https://alicezheng.org/papers/wsdm16-cdae.pdf

    • https://github.com/gtshs2/Collaborative-Denoising-Auto-Encoder

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

    • https://arxiv.org/pdf/1808.03912v1.pdf

    • https://github.com/duxy-me/ConvNCF

  • Neural Graph Collaborative Filtering [SIGIR 2019]

    • https://arxiv.org/pdf/1905.08108v1.pdf

    • https://paperswithcode.com/paper/neural-graph-collaborative-filtering

  • Transnets: Learning to transform for recommendation

    • https://arxiv.org/pdf/1704.02298

    • https://github.com/rosecatherinek/TransNets

  • Metric Factorization: Recommendation beyond Matrix Factorization

    • https://arxiv.org/pdf/1802.04606.pdf

    • https://github.com/cheungdaven/metricfactorization

  • Improving Top-K Recommendation via Joint Collaborative Autoencoders

    • http://people.tamu.edu/~zhuziwei/pubs/Ziwei_WWW_2019.pdf

    • https://github.com/Zziwei/Joint-Collaborative-Autoencoder

  • Collaborative Metric Learning

    • https://github.com/changun/CollMetric

    • http://www.cs.cornell.edu/~ylongqi/paper/HsiehYCLBE17.pdf

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

    • https://www.ijcai.org/proceedings/2018/0510.pdf

    • https://github.com/cheungdaven/NeuRec

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

    • https://arxiv.org/pdf/1901.04704v1.pdf

    • https://github.com/familyld/DeepCF

  • Graph neural networks for social recommendation [WWW2019]

    • https://arxiv.org/pdf/1902.07243.pdf

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

    • https://arxiv.org/pdf/1905.13129.pdf

    • https://github.com/jennyzhang0215/STAR-GCN

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

    • http://delivery.acm.org/10.1145/3350000/3344225/p149-jadidinejad.pdf?ip=159.226.43.46&id=3344225&acc=ACTIVE SERVICE&key=33E289E220520BFB.D25FD1BB8C28ADF7.4D4702B0C3E38B35.4D4702B0C3E38B35&acm=1574690604_f26ab14b23dc0c85179633b7bd835f03

    • https://github.com/amirj/unifying_explicit_implicit

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

    • http://yongfeng.me/attach/chen-aaai2020.pdf

    • https://github.com/chenchongthu/EHCF)

可解释性推荐系统

  • Explainable Recommendation via Multi-Task Learning in Opinionated Text Data

    • https://arxiv.org/pdf/1806.03568.pdf

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

    • http://staff.ustc.edu.cn/~hexn/papers/www18-tem.pdf

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

    • http://www.thuir.cn/group/~YQLiu/publications/WWW2018_CC.pdf

    • https://github.com/chenchongthu/NARRE

Sequence-Aware Recommender Systems

Session-based Recommender Systems

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

    • https://arxiv.org/pdf/1511.06939.pdf

    • https://github.com/hidasib/GRU4Rec

  • Neural Attentive Session-based Recommendation [CIKM 2017]

    • https://arxiv.org/pdf/1711.04725.pdf

    • https://github.com/lijingsdu/sessionRec_NARM

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

    • http://ls13-www.cs.tu-dortmund.de/homepage/publications/jannach/Conference_RecSys_2017.pdf

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

    • https://dl.acm.org/ft_gateway.cfm?id=3219950&type=pdf

    • https://github.com/uestcnlp/STAMP

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

    • https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/ren-repeatnet-2019.pdf

    • https://github.com/PengjieRen/RepeatNet

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

    • https://arxiv.org/pdf/1811.00855.pdf

    • https://github.com/CRIPAC-DIG/SR-GNN

  • Streaming Session-based Recommendation [KDD 2019]

    • https://dl.acm.org/citation.cfm?id=3292500.3330839

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

    • http://www.cs.toronto.edu/~lcharlin/papers/fp4571-songA.pdf

    • https://github.com/DeepGraphLearning/RecommenderSystems/tree/master/socialRec

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

    • https://dl.acm.org/citation.cfm?id=3331322

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

    • https://web-ainf.aau.at/pub/jannach/files/Conference_RecSys_2019_sessions.pdf

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

    • https://dl.acm.org/citation.cfm?id=3346990

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

    • https://arxiv.org/pdf/1910.12781.pdf

    • https://github.com/rn5l/session-rec

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

    • https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/wang-2019-collaborative.pdf

    • https://github.com/wmeirui/CSRM_SIGIR2019

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

    • https://dl.acm.org/citation.cfm?id=3358010

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

    • https://dl.acm.org/citation.cfm?id=3358120

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

    • https://www.ijcai.org/proceedings/2019/0799.pdf

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

    • http://www.terpconnect.umd.edu/~kpzhang/paper/Variational_Session.pdf

基于Last-N的方法

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

    • http://www.sfu.ca/~jiaxit/resources/wsdm18caser.pdf

    • https://github.com/graytowne/caser_pytorch

  • Self-Attentive Sequential Recommendation [ICDM 2018]

    • https://arxiv.org/pdf/1808.09781

    • https://github.com/kang205/SASRec

  • Hierarchical Gating Networks for Sequential Recommendation

    • https://arxiv.org/pdf/1906.09217.pdf

    • https://github.com/graytowne/caser_pytorch

  • Next Item Recommendation with Self-Attention

    • https://arxiv.org/pdf/1808.06414

    • https://github.com/cheungdaven/DeepRec/blob/master/models/seq_rec/AttRec.py

序列推荐模型

  • Collaborative Memory Network for Recommendation Systems [SIGIR 2018]

  • https://arxiv.org/pdf/1804.10862

  • https://github.com/tebesu/CollaborativeMemoryNetwork

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

  • https://www.ijcai.org/proceedings/2018/0546.pdf

  • https://github.com/uctoronto/SHAN

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

  • [[https://arxiv.org/pdf/1904.04381.pdf)

  • A Large-scale Sequential Deep Matching Model for E-commerce RecommendationCIKM 2019(https://arxiv.org/pdf/1909.00385.pdf)[code]

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

  • A Dynamic Co-attention Network for Session-based Recommendation CIKM 2019(http://delivery.acm.org/10.1145/3360000/3357964/p1461-chen.pdf?ip=159.226.43.46&id=3357964&acc=OPEN&key=33E289E220520BFB.D25FD1BB8C28ADF7.4D4702B0C3E38B35.6D218144511F3437&__acm__=1573525498_2dff93fd7304076faa5b5f8c0a604c13)

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

  • A Long-Short Demands-Aware Model for Next-Item Recommendation CoRR 2019(https://arxiv.org/pdf/1903.00066.pdf)

  • Learning from History and Present : Next-item Recommendation via Discriminatively Exploiting User Behaviors KDD 2018(https://arxiv.org/pdf/1808.01075.pdf)[JD]

  • Towards Neural Mixture Recommender for Long Range Dependent User SequencesWWW 2019(https://arxiv.org/pdf/1902.08588.pdf)

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

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

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

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

基于上下文的序列推荐模型

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

  • 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(https://github.com/RUCDM/TMRN)

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

基于知识图谱的推荐模型

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

    • dataset and code : https://github.com/RUCDM/KB4Rec

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

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

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

  • KGAT: Knowledge Graph Attention Network for Recommendation KDD2019(https://arxiv.org/pdf/1905.07854v2.pdf)[code]

Reinforcement Learning Approaches

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

  • Mention Recommendation in Twitter with Cooperative Multi-Agent Reinforcement Learning SIGIR 2019(https://dl.acm.org/ft_gateway.cfm?id=3331237&ftid=2073431&dwn=1&CFID=84209421&CFTOKEN=9a641ce449e844f4-AD0A0DFF-0D57-C718-7A3C3F3EA918C999)

  • Reinforcement Learning for User Intent Prediction in Customer Service Bots SIGIR2019(https://dl.acm.org/ft_gateway.cfm?id=3331370&ftid=2073637&dwn=1&CFID=84209311&CFTOKEN=732e7121df9e7cb4-AD0489A6-BAFB-8E0B-14DD2161DC9EA0EB)

  • Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems KDD2019(https://arxiv.org/pdf/1902.05570.pdf)

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

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

Multi-behavior learning for Recommendation

  • Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation AAAI2020(http://www.thuir.cn/group/~mzhang/publications/AAAI2020-Chenchong.pdf)[code]

Multi-task learning for Recommendation

  • Entire Space Multi-Task Model: An E ective Approach for Estimating Post-Click Conversion Rate SIGIR2018(https://arxiv.org/pdf/1804.07931.pdf)

  • Conversion Rate Prediction via Post-Click Behaviour Modeling

  • Rerceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks KDD2018(https://arxiv.org/pdf/1805.10727.pdf)

  • Modeling task relationships in multi-task learning with multi-gate mixture-of-experts KDD2018(https://dl.acm.org/doi/10.1145/3219819.3220007)

  • Recommending What Video to Watch Next: A Multitask Ranking System RecSys2019(https://daiwk.github.io/assets/youtube-multitask.pdf)

Re-ranking

  • Personalized Re-ranking for Recommendation RecSys2019(https://arxiv.org/pdf/1904.06813.pdf)[code][dataset]

  • Learning a Deep Listwise Context Model for Ranking Refinement SIGIR2018(https://arxiv.org/pdf/1804.05936.pdf)[code]

Industry

CTR Prediction

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

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

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

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

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

Match

  • [Embedding] Item2Vec-Neural Item Embedding for Collaborative Filtering Microsoft 2017(https://arxiv.org/pdf/1603.04259.pdf)

  • [Embedding] DeepWalk- Online Learning of Social Representations KDD 2014(http://www.perozzi.net/publications/14_kdd_deepwalk.pdf)

  • [Embedding] LINE - Large-scale Information Network Embedding Microsoft 2015(https://arxiv.org/pdf/1503.03578.pdf)

  • [Embedding] Node2vec - Scalable Feature Learning for Networks Stanford 2016(https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf)

  • [Embedding] Structural Deep Network Embedding [KDD2016] [PDF]

  • [Embedding] Item2Vec-Neural Item Embedding for Collaborative Filtering Microsoft 2017(https://arxiv.org/pdf/1603.04259.pdf)

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

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

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

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

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

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

  • [Semantic Match] Deep Semantic Matching for Amazon Product Search WSDM 2019(https://wsdm2019-dapa.github.io/slides/05-YiweiSong.pdf)[Amazon]

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

Others

  • Latent Cross: Making Use of Context in Recurrent Recommender Systems WSDM 2018(http://delivery.acm.org/10.1145/3160000/3159727/p46-beutel.pdf?ip=159.226.43.46&id=3159727&acc=OA&key=33E289E220520BFB.D25FD1BB8C28ADF7.4D4702B0C3E38B35.5945DC2EABF3343C&__acm__=1568103183_98476c18cb349d52e835c76d85b83253)[Youtube]

  • Learning from History and Present: Next-item Recommendation via Discriminatively Exploting Users Behaviors KDD 2018(https://arxiv.org/pdf/1808.01075.pdf)

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

Alibaba papers-continuous updating

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

  • [Match] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall 2019(https://arxiv.org/pdf/1904.08030)

  • [Long and short-term] SDM: Sequential Deep Matching Model for Online Large-scale Recommender System CIKM 2019(https://arxiv.org/pdf/1909.00385v1.pdf)

  • [Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba KDD 2018(https://arxiv.org/pdf/1803.02349.pdf)

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

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

  • [Representations] Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks KDD2018(https://arxiv.org/pdf/1805.10727.pdf)

  • [exact-K recommendation] Exact-K Recommendation via Maximal Clique Optimization KDD 2019(https://arxiv.org/pdf/1905.07089.pdf)

  • [Explain]A Capsule Network for Recommendation and Explaining What You Like and Dislike SIGIR2019(https://arxiv.org/pdf/1907.00687.pdf)[code]

  • [CTR] Privileged Features Distillation for E-Commerce Recommendations Woodstock ’18(https://arxiv.org/pdf/1907.05171.pdf)

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

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

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

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

  • [CTR] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction MLR(https://arxiv.org/pdf/1704.05194)

  • [CTR] Deep Interest Evolution Network for Click-Through Rate Prediction AAAI2019(https://arxiv.org/pdf/1809.03672.pdf)

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

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

  • [CVR] Entire Space Multi-Task Model: An E ective Approach for Estimating Post-Click Conversion Rate SIGIR2018(https://arxiv.org/pdf/1804.07931.pdf)


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