{"id":13676716,"url":"https://github.com/OnYuKang/Recommendation-systems-paperlist","last_synced_at":"2025-04-29T07:33:14.031Z","repository":{"id":100969093,"uuid":"155873046","full_name":"OnYuKang/Recommendation-systems-paperlist","owner":"OnYuKang","description":"Papers about recommendation systems that I am interested in","archived":false,"fork":false,"pushed_at":"2020-03-17T07:44:12.000Z","size":83,"stargazers_count":363,"open_issues_count":2,"forks_count":78,"subscribers_count":17,"default_branch":"master","last_synced_at":"2024-11-11T18:43:18.674Z","etag":null,"topics":["collaborative-filtering","deep-learning","explainable-recommendations","multi-armed-bandit","recommendation","recommender-system","session-based-recommendation-system","social-network","survey"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/OnYuKang.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2018-11-02T13:54:45.000Z","updated_at":"2024-10-01T08:56:28.000Z","dependencies_parsed_at":null,"dependency_job_id":"12a86acf-aa41-4ca9-aadf-5dd2f44b7875","html_url":"https://github.com/OnYuKang/Recommendation-systems-paperlist","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OnYuKang%2FRecommendation-systems-paperlist","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OnYuKang%2FRecommendation-systems-paperlist/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OnYuKang%2FRecommendation-systems-paperlist/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OnYuKang%2FRecommendation-systems-paperlist/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/OnYuKang","download_url":"https://codeload.github.com/OnYuKang/Recommendation-systems-paperlist/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251456058,"owners_count":21592285,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["collaborative-filtering","deep-learning","explainable-recommendations","multi-armed-bandit","recommendation","recommender-system","session-based-recommendation-system","social-network","survey"],"created_at":"2024-08-02T13:00:31.730Z","updated_at":"2025-04-29T07:33:09.018Z","avatar_url":"https://github.com/OnYuKang.png","language":null,"funding_links":[],"categories":["Others"],"sub_categories":[],"readme":"# Recommendation_systems_paperlist \n![GitHub stars](https://img.shields.io/github/stars/OnYuKang/Recommendation-systems-paperlist.svg?style=plastic) ![GitHub forks](https://img.shields.io/github/forks/OnYuKang/Recommendation-systems-paperlist.svg?color=black\u0026style=plastic) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=plastic)](http://makeapullrequest.com)\n\n## Survey paper\n* Recommender systems survey [Knowledge-based systems 2013]\n* Deep Learning based Recommender System: A Survey and New Perspectives [2017]\n* A Survey on Session-based Recommender System [2019] [[__pdf__](https://arxiv.org/pdf/1902.04864.pdf)]\n\n## Recommendation Systems with Social Information \n* SoRec: Social Recommendation Using Probabilistic Matrix Factorization [CIKM 2008]\n* A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks [RecSys 2010]\n* Recommender systems with social regularization [WSDM 2011]\n* On Deep Learning for Trust-Aware Recommendations in Social Networks [IEEE 2017]\n* Learning to Rank with Trust and Distrust in Recommender Systems [RecSys 2017]\n* Social Attentional Memory Network: Modeling Aspect- and Friend-level Differences in Recommendation [WSDM 2019]\n    - code : https://github.com/chenchongthu/SAMN\n* Session-based Social Recommendation via Dynamic Graph Attention Networks [WSDM 2019]\n  - code : https://github.com/DeepGraphLearning/RecommenderSystems/tree/master/socialRec\n* Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems [WWW 2019]\n* Heterogeneous Graph Attention Network [WWW 2019]\n* Graph Neural Networks for Social Recommendation [WWW 2019]\n* GhostLink: Latent Network Inference for Influence-aware Recommendation [WWW 2019]\n* SamWalker: Social Recommendation with Informative Sampling Strategy [WWW 2019]\n* Social Recommendation with Optimal Limited Attention [KDD 2019]\n* Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction [KDD 2019]\n\n## Recommendation Systems with Text Information\n  ### Topic-based approach\n  * Collaborative topic modeling for recommending scientific articles [KDD 2011]\n    - code : https://github.com/blei-lab/ctr\n  * Hidden factors and hidden topics: understanding rating dimensions with review text [RecSys 2013]\n    - code : https://github.com/lipiji/HFT\n  * Jointly modeling aspects, ratings and sentiments for movie recommendation [KDD 2014]\n    - code : https://github.com/nihalb/JMARS\n  * Ratings meet reviews, a combined approach to recommend [RecSys 2014]\n  * Exploring User-Specific Information in Music Retrieval [SIGIR 2018]\n  * Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews [WWW 2018]\n    - code : https://github.com/hustlingchen/ALFM\n  * Exploiting Ratings, Reviews and Relationships for Item Recommendations in Topic Based Social Networks [WWW 2019]\n  \n  ### Deep learning-based approach\n  * Collaborative deep learning for recommender systems [KDD 2015]\n    - code : https://github.com/js05212/CDL\n  * Convolutional Matrix Factorization for Document Context-Aware Recommendation [RecSys 2016]\n    - code : https://github.com/cartopy/ConvMF\n  * Joint Deep Modeling of Users and Items Using Reviews for Recommendation [WSDM 2017]\n    - code : https://github.com/chenchongthu/DeepCoNN\n  * Transnets: Learning to transform for recommendation [RecSys 2017]\n    - code : https://github.com/rosecatherinek/TransNets\n  * Latent Cross: Making Use of Context in Recurrent Recommender Systems [WSDM 2018]\n  * Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews [WWW 2018]\n  * Neural Attentional Rating Regression with Review-level Explanations [WWW 2018]\n    - code : https://github.com/chenchongthu/NARRE\n  * Learning Personalized Topical Compositions with Item Response Theory [WSDM 2019]\n  * Uncovering Hidden Structure in Sequence Data via Threading Recurrent Models [WSDM 2019]\n  * Gated Attentive-Autoencoder for Content-Aware Recommendation [WSDM 2019]\n    - code : https://github.com/allenjack/GATE\n  * DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation [KDD 2019]   \n  * Attentive Aspect Modeling for Review-Aware Recommendation [TOIS 2019] \n     - code : https://github.com/XinyuGuan01/Attentive-Aspect-based-Recommendation-Model\n  * Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network [EMNLP 2019]\n  \n## Explainable Recommendation Systems\n* Social Collaborative Viewpoint Regression with Explainable Recommendations [WSDM 2017]\n* Explainable Recommendation via Multi-Task Learning in Opinionated Text Data [SIGIR 2018]\n* TEM: Tree-enhanced Embedding Model for Explainable Recommendation [WWW 2018]\n* Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects [EMNLP 2019]\n* Dynamic Explainable Recommendation based on Neural Attentive Models [AAAI 2019]\n\n## Session-Based Recommendation Systems\n### Markov-chain based approach\n* Factorizing Personalized Markov Chains for Next-Basket Recommendation [WWW 2010]\n* Where You Like to Go Next: Successive Point-of-Interest Recommendation [IJCAI 2013]\n* Learning Hierarchical Representation Model for NextBasket Recommendation [SIGIR 2015]\n* Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation [ICDM 2016]\n  - code : https://github.com/stathwang/FOSSIL\n* Translation-based Recommendation [RecSys 2017]\n  - code : https://drive.google.com/file/d/0B9Ck8jw-TZUEVmdROWZKTy1fcEE/view\n    \n### RNN based approach\n* Session-based Recommendations with Recurrent Neural Networks [ICLR 2016]\n  - code : https://github.com/hidasib/GRU4Rec\n* Neural Attentive Session-based Recommendation [CIKM 2017]\n  - code : https://github.com/lijingsdu/sessionRec_NARM\n* Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks [RecSys 2017]\n* When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation [RecSys 2017]\n* Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture [RecSys 2017]\n* Learning from History and Present: Next-item Recommendation via Discriminatively Exploting Users Behaviors [KDD 2018]\n* Recurrent Neural Networks with Top-k Gains for Session-based Recommendations [CIKM 2018]\n* Hierarchical Context enabled Recurrent Neural Network for Recommendation. [AAAI 2019] \n* RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation [AAAI 2019]\n  - code : https://github.com/PengjieRen/RepeatNet\n* Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions [WSDM 2019]\n  - code : https://github.com/BjornarVass/Recsys\n* Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks [KDD 2019]\n* AIR: Attentional Intention-Aware Recommender Systems [ICDE 2019]\n\n### CNN based approach \n* 3D Convolutional Networks for Session-based Recommendation with Content Features [RecSys 2017]\n* Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding [WSDM 2018]\n  - code : https://github.com/graytowne/caser_pytorch [Pytorch]\n  - code : https://github.com/graytowne/caser [Matlab]\n* Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems [WWW 2019]\n* A Simple Convolutional Generative Network for Next Item Recommendation [WSDM 2019]\n  - code : https://github.com/graytowne/caser_pytorch\n  \n### Graph based approach\n* Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba [KDD 2018]\n* Graph Convolutional Neural Networks for Web-Scale Recommender Systems [KDD 2018]\n* Session-based Recommendation with Graph Neural Networks [AAAI 2019]\n  - code : https://github.com/CRIPAC-DIG/SR-GNN\n* Session-based Social Recommendation via Dynamic Graph Attention Networks [WSDM 2019]\n  - code : https://github.com/DeepGraphLearning/RecommenderSystems/tree/master/socialRec  \n* Graph Contextualized Self-Attention Network for Session-based Recommendation [IJCAI 2019]\n\n### Other approach\n* Diversifying Personalized Recommendation with User-session Context [IJCAI 2017]\n* Translation-based Factorization Machines for Sequential Recommendation [RecSys 2018]\n* Attention-Based Transactional Context Embedding for Next-Item Recommendation [AAAI 2018]\n* STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation [KDD 2018]\n  - code : https://github.com/uestcnlp/STAMP\n* Self-Attentive Sequential Recommendation [ICDM 2018]\n  - code : https://github.com/kang205/SASRec\n* Taxonomy-aware Multi-hop Reasoning Networks for Sequential Recommendation [WSDM 2019]\n  - code : https://github.com/RUCDM/TMRN\n* Hierarchical Neural Variational Model for Personalized Sequential Recommendation [WWW 2019]\n* BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer [CIKM 2019]\n* Hierarchical Gating Networks for Sequential Recommendation [KDD 2019]\n* Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics [KDD 2019]\n* Streaming Session-based Recommendation [KDD 2019]\n* Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit [KDD 2019]\n\n### News Recommendation\n* Google news personalization: scalable online collaborative filtering [WWW 2007]\n* Personalized News Recommendation Based on Click Behavior [IUI 2009]\n* Personalized News Recommendation Using Twitter [IEEE 2013]\n* Recommending Personalized News in Short User Sessions [RecSys 2017]\n* Embedding-based News Recommendation for Millions of Users [KDD 2017]\n* DKN: Deep Knowledge-Aware Network for News Recommendation [WWW 2018] \n* NPA: Neural News Recommendation with Personalized Attention [KDD 2019]\n  - code : https://github.com/wuch15/KDD-NPA\n* Neural News Recommendation with Heterogeneous User Behavior [EMNLP 2019]\n* Neural News Recommendation with Multi-Head Self-Attention [EMNLP 2019]\n\n### Video Recommendation\n* Video suggestion and discovery for youtube: taking random walks through the view graph [WWW 2008]\n* The YouTube Video Recommendation System [RecSys 2010]\n* Deep Neural Networks for YouTube Recommendations [RecSys 2016]\n* Wide \u0026 Deep Learning for Recommender Systems [DLRS 2016]\n* Content-based Related Video Recommendations [NIPS 2016]\n\n### Music Recommendation\n* Playlist prediction via metric embedding [KDD 2012]\n* Deep content-based music recommendation [NIPS 2013]\n* Improving Content-based and Hybrid Music Recommendation using Deep Learning [MM 2014]\n* Content-aware collaborative music recommendation using pre-trained neural networks [ISMIR 2015] \n\n### Automatic Playlist Continuation\n* Two-stage Model for Automatic Playlist Continuation at Scale [RecSys 2018]\n  - code : https://github.com/layer6ai-labs/RecSys2018\n* MMCF: Multimodal Collaborative Filtering for Automatic Playlist Continuation [RecSys 2018]\n  - code : https://github.com/hojinYang/spotify_recSys_challenge_2018\n* Artist-driven layering and user’s behaviour impact on recommendations in a playlist continuation scenario [RecSys 2018]\n  - code : https://github.com/tmscarla/spotify-recsys-challenge\n* A hybrid two-stage recommender system for automatic playlist continuation [RecSys 2018]\n  - code : https://github.com/VasiliyRubtsov/recsys2018\n\n### Route Recommendation\n* Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation [KDD 2019]\n* Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation [KDD 2019]\n\n### Image Recommendation\n* Pagerank for product image search [WWW 2008]\n* Related Pins at Pinterest: The Evolution of a Real-World Recommender System [WWW 2017]\n* Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time [WWW 2018]\n\n## Time-aware Recommendation (Temporal Dynamics)\n* Time Weight Collaborative Filtering [CIKM 2005]\n* Collaborative Filtering with Temporal Dynamics [KDD 2009]\n* Opportunity Models for E-commerce Recommendation: Right Product, Right Time [SIGIR 2013] \n* Multi-rate deep learning for temporal recommendation [SIGIR 2016]\n* Recurrent Recommender Networks [WSDM 2017]\n* Recurrent Recommendation with Local Coherence [WSDM 2019]\n\n## Reinforcement Learning\n* Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning [NeurlPS 2019]\n* Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation [NeurlPS 2019]\n\n### Multi-Armed Bandit\n* A Contextual-Bandit Approach to Personalized News Article Recommendation [WWW 2010]\n* A survey of online experiment design with the stochastic multi-armed bandit [2015] [[__pdf__](https://arxiv.org/pdf/1510.00757.pdf)]\n* Collaborative filtering as a multi-armed bandit [NIPS 2015]\n* Online Context-Aware Recommendation with Time Varying Multi-Arm Bandit [KDD 2016]\n* Collaborative Filtering Bandits [SIGIR 2016]\n\n## Cold-start Problem\n* MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation [KDD 2019]\n\n## Out of Category\n* Learning Multiple Similarities of Users and Items in Recommender Systems [ICDM 2017]\n* Neural Collaborative Filtering [WWW 2017]\n* MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings [ECML-PKDD 2017]\n* A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation [RecSys 2017]\n* IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models [SIGIR 2017]\n  - code : https://github.com/geek-ai/irgan\n* Collaborative Memory Network for Recommendation Systems [SIGIR 2018]\n  - code : https://github.com/tebesu/CollaborativeMemoryNetwork\n* Variational Autoencoders for Collaborative Filtering [WWW 2018]\n* Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking [WWW 2018]\n* Causal Embeddings for Recommendation [RecSys 2018] \n  - https://github.com/criteo-research/CausE\n* Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items [WSDM 2019]\n  - https://github.com/VRM1/WSDM19\n* RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation [WSDM 2019]\n  - https://github.com/nikolakopoulos/RecWalk\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FOnYuKang%2FRecommendation-systems-paperlist","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FOnYuKang%2FRecommendation-systems-paperlist","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FOnYuKang%2FRecommendation-systems-paperlist/lists"}