Awesome-Recommender-System

This repo will be kept updated in each week to catch up with the direction of recommender system. There is no doubt to star this repo to watch which papers are updated each week, which helps you reduce your time wasting on searching for papers of high quality in a few top conferences and journals.

POI Recommender System:

• (IJCAI2017)Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking
• (IJCAI2017) Learning user's intrinsic and extrinsic interests for point-of-interest recommendation: a unified approach
• (UbiComp2019)Privacy-preserving Cross-domain Location Recommendation
• (UbiComp 2019)From Fingerprint to Footprint: Cold-start Location Recommendation by Learning User Interest from App Data
• (IJCAI2019)Geo-ALM: POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism
• (WWW2019)Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach
• (ICDE2019)A Joint Context-Aware Embedding for Trip Recommendations

Sequential Recommender System:

• (WSDM2017)Recurrent Recommender Networks
• (IJCAI2018)Sequential Recommender System based on Hierarchical Attention Network
• (ICDM2018)Self-Attentive Sequential Recommendation
• (SIGIR2019)A Long-Short Demands-Aware Model for Next-Item Recommendation
• (SIGIR2019)Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
• (CIKM2019)Session-Based Social Recommendation via Dynamic Graph Attention Networks
• (WSDM2019)Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation
• (WSDM2019)Session-based Social Recommendation via Dynamic Graph Attention Networks
• (WWW2019)Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
• (KDD2019)Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination
• (KDD2019)Hierarchical Gating Networks for Sequential Recommendation
• (KDD2019)POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
• (PAKDD2019)A Novel Hybrid Sequential Model for Review-Based Rating Prediction
• (MM2019)Explainable Interaction-driven User Modeling over Knowl-edge Graph for Sequential Recommendation
• (IS2019)GPS: Factorized group preference-based similarity models for sparse sequential recommendation
• (CIKM2019)CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
• (IJCAI2019)Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation
• (IJCAI2019)Feature-level Deeper Self-Attention Network for Sequential Recommendation
• (WWW2020)Beyond Clicks: Modeling Multi-Relational Item Graph for Session-based Target Behavior Prediction
• (WWW2020)Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation
• (WWW2020) Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
• (WWW2020)Attentive Sequential Models of Latent Intent for Next Item Recommendation
• (WSDM2020)Time Interval Aware Self-Attention for Sequential Recommendation
• (WSDM2020)Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
• (IJCAI2020)Deep Feedback Network for Recommendation

Graph-based Recommender System:

• Rich-Item Recommendations for Rich-Users via GCNN: Exploiting Dynamic and Static Side Information
• (KDD2018)Graph Convolutional Matrix Completion
• (MM2019)MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
• (TKDE2019)Heterogeneous Information Network Embedding for Recommendation
• (SIGIR2019)SocialGCN An Efficient Graph Convolutional Network
• (SIGIR2019)Neural Graph Collaborative Filtering
• (WSDM2019)Session-based Social Recommendation via Dynamic Graph Attention Networks
• (AAAI2019)Explainable Reasoning over Knowledge Graphs for Recommendation
• (WWW2019)Graph Neural Networks for Social Recommendation
• (WWW2019)Collaborative Similarity Embedding for Recommender Systems
• (WWW2019)Unifying Knowledge Graph Learning and Recommendation Towards a Better Understanding of User Preferences
• (KDD2019)KGAT Knowledge Graph Attention Network for Recommendation
• (IJCAI2019)Unified Embedding Model over Heterogeneous Information Network for Personalized Recommendation
• (TKDE2019)Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
• (CIKM2019)Rethinking the ItemOrder in Session-based Recommendation with Graph Neural Networks
• (SDM2020)Stacked Mixed-Order Graph Convolutional Networks for Collaborative Filtering
• (AAAI2020)Multi-Component Graph Convolutional Collaborative Filtering
• (IPM2020)Graph neural news recommendation with long-term and short-term interest modeling
• (AAAI2020)Revisiting Graph based Collaborative Filtering : A Linear Residual Graph Convolutional Network Approach
• (AAAI2020)Memory Augmented Graph Neural Networks for Sequential Recommendation
• (WWW2020)Beyond Clicks: Modeling Multi-Relational Item Graph for Session-based Target Behavior Prediction
• (WWW2020)Graph Enhanced Representation Learning for News Recommendation
• (WSDM2020)DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks
• Modelling High-Order Social Relations for Item Recommendation
• (KDD2020)M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems

Review-based Recommender System:

• (WSDM2017)Joint Deep Modeling of Users and Items Using Reviews for Recommendation
• (CIKM2018)ANR:Aspect-based Neural Recommender
• (KDD2018)Multi-Pointer Co-Attention Networks for Recommendation
• (RecSys2018)Why I like it Multi-task Learning for Recommendation and Explanation
• (WWW2018)Neural attentional rating regression with review-level explanations
• (NAACL-HIT2019)Hierarchical User and Item Representation with Three-Tier Attention for Recommendation
• (RecSys2019)A Generative Model for Review-Based Recommendations
• (UMAP2019)Justifying Recommendations through Aspect-based Sentiment Analysis of Users’ Reviews
• (WWW2019)From Free-text User Reviews to Product Recommendation using Paragraph Vectors and Matrix Factorization
• (SIGIR2019)NRPA: Neural Recommendation with Personalized Attention
• (KDD2019)DAML Dual Attention Mutual Learning between Ratings and Reviews for recommendation
• (KDD2019)NPA: Neural News Recommendation with Personalized Attention
• (IJCAI2019)A Review-Driven Neural Model for Sequential Recommendation
• (IJCAI2019)Neural News Recommendation with Attentive Multi-View Learning
• (IJCAI2019)Co-Attentive Multi-Task Learning for Explainable Recommendation

Deep Learning based Recommender System:

• (TOIS2017)Version-Aware Rating Prediction for Mobile App Recommendation
• (KDD2018)Multi-Pointer Co-Attention Networks for Recommendation
• Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
• (IJCAI2018)DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation
• (SIGIR2018)An Attribute-aware Neural Attentive Model for Next Basket Recommendation
• (CIKM2018)Local and Global Information Fusion for Top-N Recommendation in Heterogeneous Information Network
• (KDD2018)Leveraging Meta-path based Context for Top-N Recommendation with A Neural Co-Attention Model
• (TOIS2019)Deep Item-based Collaborative Filtering for Top-N Recommendation
• (WSDM2019)Gated Attentive-Autoencoder for Content-Aware Recommendation
• (WWW2019)Towards Neural Mixture Recommender for Long Range Dependent User Sequences
• (WWW2019)Feature generation by convolutional neural network for click-through rate prediction
• (SDM2019)Multiplex Memory Network for Collaborative Filtering
• (IJCAI2019)Deep Adversarial Social Recommendation
• (TKDE2020)A 2-GCN: An Attribute-aware Attentive GCN Model for Recommendation
• (WSDM2020)LARA Attribute-to-feature Adversarial Learning for New-item Recommendation
• (WSDM2020)Adversarial Learning to Compare Self-Attentive Prospective Customer Recommendation in Location based Social Networks
• (AAAI2020)Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation
• (WWW2020)Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation
• (KDD2020)Controllable Multi-Interest Framework for Recommendation

Evaluation of Recommender System

• (RecSys2018)Explore, exploit, and explain personalizing explainable recommendations with bandits
• (RecSys2018)Providing explanations for recommendations in reciprocal environments
• (RecSys2018)Why I like it multi-task learning for recommendation and explanation
• (RecSys2018)Enhancing structural diversity in social network by Recommending Weak Ties
• (CHI2018)Flexible Learning with Semantic Visual Exploration and Sequence-Based Recommendation of MOOC Videos
• (IUI2019)Personalized Explanations for Hybrid Recommender Systems
• (RecSys2019)Explaining and Exploring Job Recommendations: a User-driven Approach for Interacting with Knowledge-based Job Recommender Systems
• (CHI2019)VizML A Machine Learning Approach to visualization recommendation
• (RecSys2019)Efficient Privacy Preserving Recommendations based on Social Graphs
• (UMAP2019)Justifying Recommendations through Aspect-based Sentiment Analysis of Users’ Reviews
• (UMUAI2019)Affective recommender systems in online news industry, how emotions influence reading chocies

CTR Prediction

• Deep & Cross Network for Ad Click Predictions
• Deep interest network for click-through rate prediction
• Deep Session Interest Network for Click-Through Rate Prediction
• Practical Lessons from Predicting Clicks on Ads at Facebook
• Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
• Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
• Real-time Personalization using Embeddings for Search Ranking at Airbnb
• FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
• HoAFM: A High-order Attentive Factorization Machine for CTR Prediction
• (WWW2019)Feature generation by convolutional neural network for click-through rate prediction
• (MM2019)Time-aware Session Embedding for Click-Through-Rate Prediction
• (DLP-KDD2019)Res-embedding for Deep Learning Based Click-rough Rate Prediction Modeling
• (AAAI2020)Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution

Review

• Graph Learning Approaches to Recommender Systems: A Review