OnYuKang / Recommendation Systems Paperlist
Papers about recommendation systems that I am interested in
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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]
- Session-based Social Recommendation via Dynamic Graph Attention Networks [WSDM 2019]
- 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]
- Hidden factors and hidden topics: understanding rating dimensions with review text [RecSys 2013]
- Jointly modeling aspects, ratings and sentiments for movie recommendation [KDD 2014]
- 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]
- 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]
- Convolutional Matrix Factorization for Document Context-Aware Recommendation [RecSys 2016]
- Joint Deep Modeling of Users and Items Using Reviews for Recommendation [WSDM 2017]
- Transnets: Learning to transform for recommendation [RecSys 2017]
- 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]
- 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]
- DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation [KDD 2019]
- Attentive Aspect Modeling for Review-Aware Recommendation [TOIS 2019]
- 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]
- Translation-based Recommendation [RecSys 2017]
RNN based approach
- Session-based Recommendations with Recurrent Neural Networks [ICLR 2016]
- Neural Attentive Session-based Recommendation [CIKM 2017]
- 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]
- Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions [WSDM 2019]
- 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]
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]
- Session-based Social Recommendation via Dynamic Graph Attention Networks [WSDM 2019]
- 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]
- Self-Attentive Sequential Recommendation [ICDM 2018]
- Taxonomy-aware Multi-hop Reasoning Networks for Sequential Recommendation [WSDM 2019]
- 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]
- 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]
- MMCF: Multimodal Collaborative Filtering for Automatic Playlist Continuation [RecSys 2018]
- Artist-driven layering and user’s behaviour impact on recommendations in a playlist continuation scenario [RecSys 2018]
- A hybrid two-stage recommender system for automatic playlist continuation [RecSys 2018]
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]
- Collaborative Memory Network for Recommendation Systems [SIGIR 2018]
- 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]
- Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items [WSDM 2019]
- RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation [WSDM 2019]
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