Recommendation_systems_paperlist

Survey paper

• Recommender systems survey [Knowledge-based systems 2013]
• Deep Learning based Recommender System: A Survey and New Perspectives [2017]
• A Survey on Session-based Recommender System [2019] [pdf]

Recommendation Systems with Social Information

• SoRec: Social Recommendation Using Probabilistic Matrix Factorization [CIKM 2008]
• A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks [RecSys 2010]
• Recommender systems with social regularization [WSDM 2011]
• On Deep Learning for Trust-Aware Recommendations in Social Networks [IEEE 2017]
• Learning to Rank with Trust and Distrust in Recommender Systems [RecSys 2017]
• Social Attentional Memory Network: Modeling Aspect- and Friend-level Differences in Recommendation [WSDM 2019]
  • code : https://github.com/chenchongthu/SAMN
• Session-based Social Recommendation via Dynamic Graph Attention Networks [WSDM 2019]
  • code : https://github.com/DeepGraphLearning/RecommenderSystems/tree/master/socialRec
• Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems [WWW 2019]
• Heterogeneous Graph Attention Network [WWW 2019]
• Graph Neural Networks for Social Recommendation [WWW 2019]
• GhostLink: Latent Network Inference for Influence-aware Recommendation [WWW 2019]
• SamWalker: Social Recommendation with Informative Sampling Strategy [WWW 2019]
• Social Recommendation with Optimal Limited Attention [KDD 2019]
• Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction [KDD 2019]

Recommendation Systems with Text Information

Topic-based approach

• Collaborative topic modeling for recommending scientific articles [KDD 2011]
  • code : https://github.com/blei-lab/ctr
• Hidden factors and hidden topics: understanding rating dimensions with review text [RecSys 2013]
  • code : https://github.com/lipiji/HFT
• Jointly modeling aspects, ratings and sentiments for movie recommendation [KDD 2014]
  • code : https://github.com/nihalb/JMARS
• Ratings meet reviews, a combined approach to recommend [RecSys 2014]
• Exploring User-Specific Information in Music Retrieval [SIGIR 2018]
• Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews [WWW 2018]
  • code : https://github.com/hustlingchen/ALFM
• Exploiting Ratings, Reviews and Relationships for Item Recommendations in Topic Based Social Networks [WWW 2019]

Deep learning-based approach

• Collaborative deep learning for recommender systems [KDD 2015]
  • code : https://github.com/js05212/CDL
• Convolutional Matrix Factorization for Document Context-Aware Recommendation [RecSys 2016]
  • code : https://github.com/cartopy/ConvMF
• Joint Deep Modeling of Users and Items Using Reviews for Recommendation [WSDM 2017]
  • code : https://github.com/chenchongthu/DeepCoNN
• Transnets: Learning to transform for recommendation [RecSys 2017]
  • code : https://github.com/rosecatherinek/TransNets
• Latent Cross: Making Use of Context in Recurrent Recommender Systems [WSDM 2018]
• Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews [WWW 2018]
• Neural Attentional Rating Regression with Review-level Explanations [WWW 2018]
  • code : https://github.com/chenchongthu/NARRE
• Learning Personalized Topical Compositions with Item Response Theory [WSDM 2019]
• Uncovering Hidden Structure in Sequence Data via Threading Recurrent Models [WSDM 2019]
• Gated Attentive-Autoencoder for Content-Aware Recommendation [WSDM 2019]
  • code : https://github.com/allenjack/GATE
• DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation [KDD 2019]
• Attentive Aspect Modeling for Review-Aware Recommendation [TOIS 2019]
  • code : https://github.com/XinyuGuan01/Attentive-Aspect-based-Recommendation-Model
• Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network [EMNLP 2019]

Explainable Recommendation Systems

• Social Collaborative Viewpoint Regression with Explainable Recommendations [WSDM 2017]
• Explainable Recommendation via Multi-Task Learning in Opinionated Text Data [SIGIR 2018]
• TEM: Tree-enhanced Embedding Model for Explainable Recommendation [WWW 2018]
• Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects [EMNLP 2019]
• Dynamic Explainable Recommendation based on Neural Attentive Models [AAAI 2019]

Session-Based Recommendation Systems

Markov-chain based approach

• Factorizing Personalized Markov Chains for Next-Basket Recommendation [WWW 2010]
• Where You Like to Go Next: Successive Point-of-Interest Recommendation [IJCAI 2013]
• Learning Hierarchical Representation Model for NextBasket Recommendation [SIGIR 2015]
• Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation [ICDM 2016]
  • code : https://github.com/stathwang/FOSSIL
• Translation-based Recommendation [RecSys 2017]
  • code : https://drive.google.com/file/d/0B9Ck8jw-TZUEVmdROWZKTy1fcEE/view

RNN based approach

• Session-based Recommendations with Recurrent Neural Networks [ICLR 2016]
  • code : https://github.com/hidasib/GRU4Rec
• Neural Attentive Session-based Recommendation [CIKM 2017]
  • code : https://github.com/lijingsdu/sessionRec_NARM
• Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks [RecSys 2017]
• When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation [RecSys 2017]
• Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture [RecSys 2017]
• Learning from History and Present: Next-item Recommendation via Discriminatively Exploting Users Behaviors [KDD 2018]
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations [CIKM 2018]
• Hierarchical Context enabled Recurrent Neural Network for Recommendation. [AAAI 2019]
• RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation [AAAI 2019]
  • code : https://github.com/PengjieRen/RepeatNet
• Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions [WSDM 2019]
  • code : https://github.com/BjornarVass/Recsys
• Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks [KDD 2019]
• AIR: Attentional Intention-Aware Recommender Systems [ICDE 2019]

CNN based approach

• 3D Convolutional Networks for Session-based Recommendation with Content Features [RecSys 2017]
• Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding [WSDM 2018]
  • code : https://github.com/graytowne/caser_pytorch [Pytorch]
  • code : https://github.com/graytowne/caser [Matlab]
• Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems [WWW 2019]
• A Simple Convolutional Generative Network for Next Item Recommendation [WSDM 2019]
  • code : https://github.com/graytowne/caser_pytorch

Graph based approach

• Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba [KDD 2018]
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems [KDD 2018]
• Session-based Recommendation with Graph Neural Networks [AAAI 2019]
  • code : https://github.com/CRIPAC-DIG/SR-GNN
• Session-based Social Recommendation via Dynamic Graph Attention Networks [WSDM 2019]
  • code : https://github.com/DeepGraphLearning/RecommenderSystems/tree/master/socialRec
• Graph Contextualized Self-Attention Network for Session-based Recommendation [IJCAI 2019]

Other approach

• Diversifying Personalized Recommendation with User-session Context [IJCAI 2017]
• Translation-based Factorization Machines for Sequential Recommendation [RecSys 2018]
• Attention-Based Transactional Context Embedding for Next-Item Recommendation [AAAI 2018]
• STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation [KDD 2018]
  • code : https://github.com/uestcnlp/STAMP
• Self-Attentive Sequential Recommendation [ICDM 2018]
  • code : https://github.com/kang205/SASRec
• Taxonomy-aware Multi-hop Reasoning Networks for Sequential Recommendation [WSDM 2019]
  • code : https://github.com/RUCDM/TMRN
• Hierarchical Neural Variational Model for Personalized Sequential Recommendation [WWW 2019]
• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer [CIKM 2019]
• Hierarchical Gating Networks for Sequential Recommendation [KDD 2019]
• Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics [KDD 2019]
• Streaming Session-based Recommendation [KDD 2019]
• Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit [KDD 2019]

News Recommendation

• Google news personalization: scalable online collaborative filtering [WWW 2007]
• Personalized News Recommendation Based on Click Behavior [IUI 2009]
• Personalized News Recommendation Using Twitter [IEEE 2013]
• Recommending Personalized News in Short User Sessions [RecSys 2017]
• Embedding-based News Recommendation for Millions of Users [KDD 2017]
• DKN: Deep Knowledge-Aware Network for News Recommendation [WWW 2018]
• NPA: Neural News Recommendation with Personalized Attention [KDD 2019]
  • code : https://github.com/wuch15/KDD-NPA
• Neural News Recommendation with Heterogeneous User Behavior [EMNLP 2019]
• Neural News Recommendation with Multi-Head Self-Attention [EMNLP 2019]

Video Recommendation

• Video suggestion and discovery for youtube: taking random walks through the view graph [WWW 2008]
• The YouTube Video Recommendation System [RecSys 2010]
• Deep Neural Networks for YouTube Recommendations [RecSys 2016]
• Wide & Deep Learning for Recommender Systems [DLRS 2016]
• Content-based Related Video Recommendations [NIPS 2016]

Music Recommendation

• Playlist prediction via metric embedding [KDD 2012]
• Deep content-based music recommendation [NIPS 2013]
• Improving Content-based and Hybrid Music Recommendation using Deep Learning [MM 2014]
• Content-aware collaborative music recommendation using pre-trained neural networks [ISMIR 2015]

Automatic Playlist Continuation

• Two-stage Model for Automatic Playlist Continuation at Scale [RecSys 2018]
  • code : https://github.com/layer6ai-labs/RecSys2018
• MMCF: Multimodal Collaborative Filtering for Automatic Playlist Continuation [RecSys 2018]
  • code : https://github.com/hojinYang/spotify_recSys_challenge_2018
• Artist-driven layering and user’s behaviour impact on recommendations in a playlist continuation scenario [RecSys 2018]
  • code : https://github.com/tmscarla/spotify-recsys-challenge
• A hybrid two-stage recommender system for automatic playlist continuation [RecSys 2018]
  • code : https://github.com/VasiliyRubtsov/recsys2018

Route Recommendation

• Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation [KDD 2019]
• Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation [KDD 2019]

Image Recommendation

• Pagerank for product image search [WWW 2008]
• Related Pins at Pinterest: The Evolution of a Real-World Recommender System [WWW 2017]
• Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time [WWW 2018]

Time-aware Recommendation (Temporal Dynamics)

• Time Weight Collaborative Filtering [CIKM 2005]
• Collaborative Filtering with Temporal Dynamics [KDD 2009]
• Opportunity Models for E-commerce Recommendation: Right Product, Right Time [SIGIR 2013]
• Multi-rate deep learning for temporal recommendation [SIGIR 2016]
• Recurrent Recommender Networks [WSDM 2017]
• Recurrent Recommendation with Local Coherence [WSDM 2019]

Reinforcement Learning

• Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning [NeurlPS 2019]
• Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation [NeurlPS 2019]

Multi-Armed Bandit

• A Contextual-Bandit Approach to Personalized News Article Recommendation [WWW 2010]
• A survey of online experiment design with the stochastic multi-armed bandit [2015] [pdf]
• Collaborative filtering as a multi-armed bandit [NIPS 2015]
• Online Context-Aware Recommendation with Time Varying Multi-Arm Bandit [KDD 2016]
• Collaborative Filtering Bandits [SIGIR 2016]

Cold-start Problem

• MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation [KDD 2019]

Out of Category

• Learning Multiple Similarities of Users and Items in Recommender Systems [ICDM 2017]
• Neural Collaborative Filtering [WWW 2017]
• MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings [ECML-PKDD 2017]
• A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation [RecSys 2017]
• IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models [SIGIR 2017]
  • code : https://github.com/geek-ai/irgan
• Collaborative Memory Network for Recommendation Systems [SIGIR 2018]
  • code : https://github.com/tebesu/CollaborativeMemoryNetwork
• Variational Autoencoders for Collaborative Filtering [WWW 2018]
• Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking [WWW 2018]
• Causal Embeddings for Recommendation [RecSys 2018]
  • https://github.com/criteo-research/CausE
• Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items [WSDM 2019]
  • https://github.com/VRM1/WSDM19
• RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation [WSDM 2019]
  • https://github.com/nikolakopoulos/RecWalk