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https://github.com/OnYuKang/Recommendation-systems-paperlist
Papers about recommendation systems that I am interested in
https://github.com/OnYuKang/Recommendation-systems-paperlist
collaborative-filtering deep-learning explainable-recommendations multi-armed-bandit recommendation recommender-system session-based-recommendation-system social-network survey
Last synced: about 2 months ago
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Papers about recommendation systems that I am interested in
- Host: GitHub
- URL: https://github.com/OnYuKang/Recommendation-systems-paperlist
- Owner: OnYuKang
- Created: 2018-11-02T13:54:45.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-03-17T07:44:12.000Z (over 4 years ago)
- Last Synced: 2024-06-17T13:34:12.931Z (3 months ago)
- Topics: collaborative-filtering, deep-learning, explainable-recommendations, multi-armed-bandit, recommendation, recommender-system, session-based-recommendation-system, social-network, survey
- Homepage:
- Size: 81.1 KB
- Stars: 362
- Watchers: 17
- Forks: 79
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Recommendation_systems_paperlist
  [](http://makeapullrequest.com)
## 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__](https://arxiv.org/pdf/1902.04864.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__](https://arxiv.org/pdf/1510.00757.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