Awesome Recsys

I share information related to the Recommender Systems that I am interested in. They consist of SIGIR, RecSys, ICLR, NeurIPS, ICML, AAAI, IJCAI, KDD, etc. If you want to see each Conference paper chronologically, Click here.

• modified : 2023-02-20

Table of contents

1. Awesome Recsys
   1. Recommendation
      1. Models
      2. 2022
      3. 2021
      4. 2020
      5. 2019
      6. 2018
      7. 2017
      8. ~2016
      9. Others
      10. Survey

Recommendation

Models

2022

• PinnerFormer: PinnerFormer: Sequence Modeling for User Representation at Pinterest (KDD’22)
• ItemSage: ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest (KDD’22)
• DuoRec: Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation (WSDM’22)
• FMLP-Rec: FilterEnhanced MLP is All You Need for Sequential Recommendation (WWW’22)
• CML: Contrastive Meta Learning with Behavior Multiplicity for Recommendation (WSDM’22)
• Tiger: Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation (CIKM’22)
• AiRS: AiRS: A Large-Scale Recommender System at NAVER News (ICDE’22)
• CoRGi: CORGI: Content-Rich Graph Neural Networks with Attention (KDD’22)

2021

• MAIL: Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems (CIKM’21)
• Transformer4Rec: Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation (Recsys’21)
• NGF: Neural graph filtering for context-aware recommendation (ACML’21)
• SGL: Self-supervised Graph Learning for Recommendation (SIGIR’21)
• MHCN: Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (WWW’21)
• DHCN: Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (AAAI’21)
• MINCE: Memory Augmented MultiInstance Contrastive Predictive Coding for Sequential Recommendation (ICDM’21)
• SEPT: Socially-Aware Self-Supervised Tri-Training for Recommendation (KDD’21)
• BUIR: Bootstrapping User and Item Representations for One-Class Collaborative Filtering (SIGIR’21)
• UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation (CIKM’21)
• MixGCF: MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems (KDD’21)
• SERec: An Efficient and Effective Framework for Session-based Social Recommendation (WSDM’21)
• CL4SRec: Contrastive Learning for Sequential Recommendation (SIGIR’21)

2020

• PinnerSage: PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest (KDD’20)
• TAFA: TAFA: Two-headed attention fused autoencoder for context-aware recommendations (Recsys’20)
• MBCN: Multi-Branch Convolutional Network for Context-Aware Recommendation (SIGIR’20)
• ENSFM: Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation (WWW’20)
• S3-Rec: S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization (CIKM’20)
• DMR: Deep Match to Rank Model for Personalized Click-Through Rate Prediction (AAAI’20)
• EHCF: Efficient heterogeneous collaborative filtering without neg-ative sampling for recommendation (AAAI’20)
• SCE-GNN: Global Context Enhanced Graph Neural Networks for Session-based Recommendation (SIGIR’20)
• SSG: Set-Sequence-Graph: A Multi-View Approach Towards Exploiting Reviews for Recommendation (CIKM’20)
• SML: Symmetric Metric Learning with Adaptive Margin for Recommendation (AAAI’20)
• KHGT: Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation (AAAI’20)
• LCF: Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters (PMLR’20)
• SEE-PT: SEE-PT: Sequential recommendation via personalized transformer (RecSys’20)
• LightGCN: LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (SIGIR’20)
• MBGCN: Multi-behavior Recommendation with Graph Convolutional Networks (SIGIR’20)
• MA-GNN: Memory Augmented Graph Neural Networks for Sequential Recommendation (AAAI’20)

2019

• LLAE: From Zero-Shot Learning to Cold-Start Recommendation (AAAI’19)
• MetaEmb: Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings (SIGIR’19)
• NMTR: Neural Multi-Task Recommendation from Multi-Behavior Data (ICDE’19)
• NGCF: Neural Graph Collaborative Filtering (SIGIR’19)
• BERT4Rec: BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (CIKM’19)
• KGAT: KGAT: Knowledge Graph Attention Network for Recommendation (KDD’19)
• KGCN: Knowledge Graph Convolutional Networks for Recommender Systems (WWW’19)
• GraphRec: Graph Neural Networks for Social Recommendation (WWW’19)
• NARRE: Neural Attentional Rating Regression with Review-level Explanations (WWW’19)
• METAS: Action Space Learning for Heterogeneous User Behavior Prediction (IJCAI’19)
• SR-GNN: Session-based recommendation with graph neural networks (AAAI’19)

2018

• Caser: Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding (WSDM’18)
• PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems (KDD’18)
• HIN: Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation (CIKM’18)
• VAE: Variational Autoencoders for Collaborative Filtering (WWW’18)
• triple2vec: Representing and Recommending Shopping Baskets with Complementarity, Compatibility and Loyalty (CIKM’18)
• GC-MC: Graph Convolutional Matrix Completion (KDD’18)
• SASRec: Self-Attentive Sequential Recommendation (ICDM’18)
• SDNets: Adversarial Distillation for Efficient Recommendation with External Knowledge (TOIS’18)
• AIN: An Attentive Interaction Network for Context-aware Recommendation (CIKM’18)
• ConvNCF: Outer Product-based Neural Collaborative Filtering (IJCAI’18)
• STAMP: STAMP: shortterm attention/memory priority model for session-based recommendation

2017

• TransNet: TransNets: Learning to Transform for Recommendation (Recsys’17)
• DeepCoNN: Joint Deep Modeling of Users and Items Using Reviews for Recommendation (WSDM’17)
• ACF: Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention (SIGIR’17)
• CML: Collaborative Metric Learning (WWW’17)
• NMF: Neural Factorization Machines for Sparse Predictive Analytics (SIGIR’17)
• DMF: Deep matrix factorization models for recommender systems (IJCAI’17)
• NARM: Neural attentive session-based recommendation (CIKM’17)
• NCF: Neural Collaborative Filtering (WWW’17)

~2016

• CDAE: Collaborative Denoising Auto-Encoders for Top-N Recommender Systems (WSDM’16)
• ConvMF: Convolutional Matrix Factorization for Document Context-Aware Recommendation (RecSys’16)
• eALS: Fast Matrix Factorization for Online Recommendation with Implicit Feedback (SIGIR’16)
• GRU4Rec: Session-based Recommendations with Recurrent Neural Networks (ICLR’16)
• AutoRec: AutoRec: Autoencoders Meet Collaborative Filtering (WWW’15)
• CDL: Collaborative Deep Learning for Recommender Systems (KDD’15)
• CSLIM: Deviation-Based Contextual SLIM Recommenders (CIKM’14)
• LogisticMF: Logistic Matrix Factorization for Implicit Feedback (NeurIPS’14)
• HFT: Hidden factors and hidden topics: understanding rating dimensions with review text (Recsys’13)
• CTR: Collaborative topic modeling for recommending scientific articles (KDD’11)
• SLIM: SLIM: Sparse Linear Methods for Top-N Recommender Systems (ICDM’11)
• MF: Matrix factorization techniques for recommender systems (MC’09)
• BPR: BPR: Bayesian Personalized Ranking from Implicit Feedback (UAI’09)
• SoRec: SoRec: Social Recommendation Using Probabilistic Matrix Factorization (CIKM’08)
• ALS: Collaborative Filtering for Implicit Feedback Datasets (ICDM’08)
• RBM: Restricted Boltzmann Machines for Collaborative Filtering (ICML’07)
• Item-Base CF: Item-based top-N recommendation algorithms (TOIS’04)

Others

• Self-supervised Learning for Large-scale Item Recommendations (CIKM’21)
• Disentangled Self-Supervision in Sequential Recommenders (KDD’20)
• The YouTube video recommendation system (Recsys’16)
• Multiverse Recommendation: N-dimensional Tensor Factorization for Context-aware Collaborative Filtering (Recsys’10)
• The Netflix prize (KDD’07)
• Amazon.com recommendations: item-to-item collaborative filtering (MIC’03)
• Item-based collaborative filtering recommendation algorithms (WWW’01)
• Learning Collaborative Information Filters (AAAI’98)
• GroupLens: An Open Architecture for Collaborative Filtering of Netnews (CSCW’94)

Survey

• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2022)
• Deep Learning Based Recommender System: A Survey and New Perspectives (2019)
• Recommender System Application Developments: A Survey (2015)

Table of contents