GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → mengfeizhang820 → Paperlist For Recommender Systems

mengfeizhang820 / Paperlist For Recommender Systems

Recommender Systems Paperlist that I am interested in

Labels

embeddingscollaborative-filtering

Projects that are alternatives of or similar to Paperlist For Recommender Systems

Recommender-Systems-with-Collaborative-Filtering-and-Deep-Learning-Techniques
Implemented User Based and Item based Recommendation System along with state of the art Deep Learning Techniques
Stars: ✭ 41 (-86.01%)
Mutual labels:  embeddings, collaborative-filtering
Awesome-Machine-Learning-Papers
📖Notes and remarks on Machine Learning related papers
Stars: ✭ 35 (-88.05%)
Mutual labels:  embeddings, collaborative-filtering
Movie Recommendation Engine
Movie Recommender based on the MovieLens Dataset (ml-100k) using item-item collaborative filtering.
Stars: ✭ 21 (-92.83%)
Mutual labels:  collaborative-filtering
Decagon
Graph convolutional neural network for multirelational link prediction
Stars: ✭ 268 (-8.53%)
Mutual labels:  embeddings
go2vec
Read and use word2vec vectors in Go
Stars: ✭ 44 (-84.98%)
Mutual labels:  embeddings
recsys spark
Spark SQL 实现 ItemCF，UserCF，Swing，推荐系统，推荐算法，协同过滤
Stars: ✭ 76 (-74.06%)
Mutual labels:  collaborative-filtering
Hetu
A high-performance distributed deep learning system targeting large-scale and automated distributed training.
Stars: ✭ 78 (-73.38%)
Mutual labels:  embeddings
entity-embed
PyTorch library for transforming entities like companies, products, etc. into vectors to support scalable Record Linkage / Entity Resolution using Approximate Nearest Neighbors.
Stars: ✭ 96 (-67.24%)
Mutual labels:  embeddings
Summary Of Recommender System Papers
阅读过的推荐系统论文的归类总结，持续更新中…
Stars: ✭ 288 (-1.71%)
Mutual labels:  collaborative-filtering
cade
Compass-aligned Distributional Embeddings. Align embeddings from different corpora
Stars: ✭ 29 (-90.1%)
Mutual labels:  embeddings
Movielens
4 different recommendation engines for the MovieLens dataset.
Stars: ✭ 265 (-9.56%)
Mutual labels:  collaborative-filtering
game2vec
TensorFlow implementation of word2vec applied on https://www.kaggle.com/tamber/steam-video-games dataset, using both CBOW and Skip-gram.
Stars: ✭ 62 (-78.84%)
Mutual labels:  embeddings
recommender
NReco Recommender is a .NET port of Apache Mahout CF java engine (standalone, non-Hadoop version)
Stars: ✭ 35 (-88.05%)
Mutual labels:  collaborative-filtering
Hub
A library for transfer learning by reusing parts of TensorFlow models.
Stars: ✭ 3,007 (+926.28%)
Mutual labels:  embeddings
Persian-Sentiment-Analyzer
Persian sentiment analysis ( آناکاوی سهش های فارسی | تحلیل احساسات فارسی )
Stars: ✭ 30 (-89.76%)
Mutual labels:  embeddings
Librec
LibRec: A Leading Java Library for Recommender Systems, see
Stars: ✭ 3,045 (+939.25%)
Mutual labels:  collaborative-filtering
FaceRecognition With FaceNet Android
Face Recognition using the FaceNet model and MLKit on Android.
Stars: ✭ 113 (-61.43%)
Mutual labels:  embeddings
Spotify-Song-Recommendation-ML
UC Berkeley team's submission for RecSys Challenge 2018
Stars: ✭ 70 (-76.11%)
Mutual labels:  collaborative-filtering
keras-word-char-embd
Concatenate word and character embeddings in Keras
Stars: ✭ 43 (-85.32%)
Mutual labels:  embeddings
Polyfuzz
Fuzzy string matching, grouping, and evaluation.
Stars: ✭ 292 (-0.34%)
Mutual labels:  embeddings
View All Similar Projects ➔

Recommender Systems Paperlist

Survey Papers

  • Deep Learning based Recommender System: A Survey and New Perspectives [2017][PDF]
  • 基于深度学习的推荐系统研究综述 [2018] [PDF]
  • Explainable Recommendation: A Survey and New Perspectives [2018] [PDF]
  • Sequence-Aware Recommender Systems [2018] [PDF]
  • DeepRec: An Open-source Toolkit for Deep Learning based Recommendation [IJCAI 2019] [PDF]
  • Graph Embedding Techniques, Applications, and Performance: A Survey [2018] [PDF]

Recommender Systems with Content Information

Review-based Approaches

  • Convolutional Matrix Factorization for Document Context-Aware Recommendation [RecSys 2016] [PDF] [code]

  • Joint Deep Modeling of Users and Items Using Reviews for Recommendation [WSDM 2017][PDF][code]

  • Multi-Pointer Co-Attention Networks for Recommendation [KDD 2018][PDF][code]

  • Gated attentive-autoencoder for content-aware recommendation [WSDM 2019][PDF][code]

Collaborative Filtering Recommendations

  • Neural Collaborative Filtering [WWW 2017][PDF][code]
  • Collaborative Denoising Auto-Encoders for Top-N Recommender Systems [pdf] [code]
  • Outer Product-based Neural Collaborative Filtering [IJCAI 2018][PDF][code]
  • Neural Graph Collaborative Filtering [SIGIR 2019] [PDF][code]
  • Transnets: Learning to transform for recommendation [RecSys 2017][PDF][code]
  • Metric Factorization: Recommendation beyond Matrix Factorization [PDF][code]
  • Improving Top-K Recommendation via Joint Collaborative Autoencoders [PDF][code]
  • Collaborative Metric Learning [WWW2017][code][PDF]
  • NeuRec : On Nonlinear Transformation for Personalized Ranking [IJACA 2018] [PDF][code]
  • DeepCF : A Unified Framework of Representation Learning and Matching Function Learning in Recommender System [AAAI2019 oral] [PDF][code]
  • Try This Instead:Personalized and Interpretable Substitute Recommendation [[SIGIR2020]] [PDF][code]
  • STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems [[IJCAI2019]] [PDF] [code]
  • Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks [[ICTIR2019]] [PDF] [code]
  • Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation [AAAI2020] [PDF][code]
  • Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach [AAAI2020] [PDF][code]
  • LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation [SIGIR2020] [PDF][code]
  • Disentangled Graph Collaborative Filtering [SIGIR2020][PDF] [code]

Explainable Recommender Systems

  • Explainable Recommendation via Multi-Task Learning in Opinionated Text Data [SIGIR 2018][PDF]
  • TEM: Tree-enhanced Embedding Model for Explainable Recommendation [WWW 2018][PDF]
  • Neural Attentional Rating Regression with Review-level Explanations [WWW 2018] [PDF][code]

Sequence-Aware Recommender Systems

Session-based Recommender Systems

  • Session-based Recommendations with Recurrent Neural Networks [ICLR 2016] [PDF][code]

  • Neural Attentive Session-based Recommendation [CIKM 2017] [PDF][code]

  • When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation [RecSys 2017][PDF]

  • STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation [KDD 2018] [PDF][code]

  • RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation [AAAI 2019][PDF][code]

  • Session-based Recommendation with Graph Neural Networks [AAAI 2019][PDF][code]

  • Streaming Session-based Recommendation [KDD 2019] [PDF]

  • Session-based Social Recommendation via Dynamic Graph Attention Networks [WSDM 2019][PDF][code]

  • Sequence and Time Aware Neighborhood for Session-based Recommendations [SIGIR 2019] [PDF]

  • Performance Comparison of Neural and Non-Neural Approaches to Session-based Recommendation [RecSys 2019][PDF]

  • Predictability Limits in Session-based Next Item Recommendation [RecSys 2019][PDF]

  • Empirical Analysis of Session-Based Recommendation Algorithms [2019] [PDF][code]

  • A Collaborative Session-based Recommendation Approach with Parallel Memory Modules [SIGIR2019][PDF] [code]

  • Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks [CIKM2019][PDF]

  • Session-based Recommendation with Hierarchical Memory Networks [CIKM2019] [PDF]

  • ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation [IJCAI2019][PDF]

  • TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation [SIGIR2020][PDF][code]

  • Star Graph Neural Networks for Session-based Recommendation[CIKM2020][PDF]

  • Session-based Recommendation with Hierarchical Leaping Networks[SIGIR2020]][PDF]

  • Handling Information Loss of Graph Neural Networks for Session-based Recommendation[KDD2020][PDF][code]

  • Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation[SIGIR2020][PDF][code]

Sequential recommendations

  • Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding [WSDM 2018][PDF][code]
  • Self-Attentive Sequential Recommendation [ICDM 2018] [PDF][code]
  • Hierarchical Gating Networks for Sequential Recommendation [KDD 2019][PDF][code]
  • Next Item Recommendation with Self-Attention [ACM 2018][PDF][code]
  • Time Interval Aware Self-Attention for Sequential Recommendation [WSDM2020][PDF]
  • Time to Shop for Valentine’s Day: Shopping Occasions and Sequential Recommendation in E-commerce [WSDM2020][PDF]
  • Disentangled Self-Supervision in Sequential Recommenders [KDD2020][PDF]
  • DynamicRec: A Dynamic Convolutional Network for Next Item Recommendation [CIKM2020][PDF][code]
  • S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [CIKM2020][PDF][code]

Long and short-term Sequential Recommendations

  • Collaborative Memory Network for Recommendation Systems [SIGIR 2018][PDF][code]
  • Sequential Recommender System based on Hierarchical Attention Network [IJCAI 2018] [PDF][code]
  • Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems [WWW 2019] [PDF]
  • A Large-scale Sequential Deep Matching Model for E-commerce Recommendation[CIKM 2019][PDF][code]
  • Recurrent Neural Networks for Long and Short-Term Sequential Recommendation [RecSys 2018] [PDF]
  • A Dynamic Co-attention Network for Session-based Recommendation [CIKM 2019][PDF]
  • A Long-Short Demands-Aware Model for Next-Item Recommendation [CoRR 2019][PDF]
  • Learning from History and Present : Next-item Recommendation via Discriminatively Exploiting User Behaviors [KDD 2018][PDF][JD]
  • A Review-Driven Neural Model for Sequential Recommendation [IJCAI 2019] [PDF]
  • Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation [IJCAI 2019] [PDF][code][Microsoft]
  • Long- and Short-term Preference Learning for Next POI Recommendation [CIKM 2019] [PDF]
  • Neural News Recommendation with Long- and Short-term User Representations [ACL 2019][Microsoft][PDF]
  • CTRec: A Long-Short Demands Evolution Model for Continuous-Time Recommendation [PDF]
  • Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group Recommendation [SIGIR2020][PDF]
  • MRIF: Multi-resolution Interest Fusion for Recommendation [SIGIR2020] [PDF]

Context-Aware Sequential Recommendations

  • Context-Aware Sequential Recommendations withStacked Recurrent Neural Networks [WWW 2019][PDF][code]

Knowledge Graph-based Recommendations

  • Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks [SIGIR 2018] [PDF]
  • DKN: Deep Knowledge-Aware Network for News Recommendation [WWW 2018] [PDF][code]
  • RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems [CIKM 2018] [PDF][code]
  • Knowledge Graph Convolutional Networks for Recommender Systems [WWW 2019] [PDF][code]
  • KGAT: Knowledge Graph Attention Network for Recommendation [KDD2019][PDF][code]

Reinforcement Learning Approaches

  • DRN: A Deep Reinforcement Learning Framework for News Recommendation [WWW 2018] [PDF]
  • Top-K Off-Policy Correction for a REINFORCE Recommender System [WSDM 2019] [PDF][[Youtube]]

Multi-behavior learning for Recommendation

  • Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation [AAAI2020][PDF][code]

Multi-Interest and Diversity learning for Recommendation

  • Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction [SIGIR2020][PDF][code]
  • Controllable Multi-Interest Framework for Recommendation [KDD2020] [PDF][code]
  • Multi-Interest Network with Dynamic Routing for Recommendation at Tmall [PDF]
  • Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks (IJCAI2020)[PDF]
  • Sequential and Diverse Recommendation with Long Tail [IJCAI2019][PDF]
  • Improving End-to-End Sequential Recommendations with Intent-aware Diversification [CIKM2020) [PDF][code]
  • A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks [KDD2020][PDF]
  • M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems[KDD2020][PDF][Alibaba Match]
  • Deep Multi-Interest Network for Click-through Rate Prediction [CIKM2020][PDF]
  • Recent Advances in Diversified Recommendation
  • Diversified Interactive Recommendation with Implicit Feedback [AAAI2020]

Multi-task learning for Recommendation

  • [ESMM]Entire Space Multi-Task Model: An E ective Approach for Estimating Post-Click Conversion Rate [SIGIR2018][PDF]
  • [ESM2]Conversion Rate Prediction via Post-Click Behaviour Modeling
  • [MMoE]Modeling task relationships in multi-task learning with multi-gate mixture-of-experts [KDD2018][PDF]
  • Recommending What Video to Watch Next: A Multitask Ranking System [RecSys2019][PDF]
  • [RecSys2020 best paper]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations[Recsys2020][PDF]

Re-ranking

  • Personalized Re-ranking for Recommendation [RecSys2019][PDF][code][dataset]
  • Learning a Deep Listwise Context Model for Ranking Refinement [SIGIR2018][PDF][code]

Industry

CTR Prediction

  • DeepFM: A Factorization-Machine based Neural Network for CTR Prediction [[IJCAI 2017] [PDF] [Huawei]

  • xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems] [KDD2018] [PDF] [Microsoft]

  • Order-aware Embedding Neural Network for CTR Prediction][SIGIR 2019] [PDF] [Huawei]

  • Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction [WWW 2019] [PDF] [Huawei]

  • Interaction-aware Factorization Machines for Recommender Systems [AAAI2019] [PDF][code][Tencent]

Match

  • [Embedding] Item2Vec-Neural Item Embedding for Collaborative Filtering [Microsoft 2017][PDF]

  • [Embedding] DeepWalk- Online Learning of Social Representations [KDD 2014][PDF]

  • [Embedding] LINE - Large-scale Information Network Embedding [Microsoft 2015][PDF]

  • [Embedding] Node2vec - Scalable Feature Learning for Networks [Stanford 2016][PDF]

  • [Embedding] Structural Deep Network Embedding [KDD2016] [PDF]

  • [Embedding] Item2Vec-Neural Item Embedding for Collaborative Filtering [Microsoft 2017][PDF]

  • [Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb [KDD 2018] [PDF]

  • [Embedding] Graph Convolutional Neural Networks for Web-Scale Recommender Systems [KDD 2018] [PDF][Pinterest]

  • Is a Single Embedding Enough ? Learning Node Representations that Capture Multiple Social Contexts [WWW 2019] [PDF]

  • [Embedding] Representation Learning for Attributed Multiplex Heterogeneous Network [KDD 2019] [PDF]

  • [DNN Match] Deep Neural Networks for YouTube Recommendations [RecSys 2016] [PDF][Youtube]

  • [DNN Match] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations[RecSys 2019] [PDF]

  • [Semantic Match] Deep Semantic Matching for Amazon Product Search [WSDM 2019][PDF][Amazon]

  • [Tree Match] Joint Optimization of Tree-based Index and Deep Model for Recommender Systems [NeurIPS 2019] [PDF][Tencent]

Others

  • Latent Cross: Making Use of Context in Recurrent Recommender Systems [WSDM 2018][PDF][Youtube]

  • Learning from History and Present: Next-item Recommendation via Discriminatively Exploting Users Behaviors [KDD 2018][PDF]

  • Real-time Attention Based Look-alike Model for Recommender System [KDD 2019] [PDF] [Tencent]

Alibaba papers-continuous updating

  • [Match] TDM：Learning Tree-based Deep Model for Recommender Systems [KDD2018] [PDF]

  • [Match] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall [2019][PDF]

  • [Long and short-term] SDM: Sequential Deep Matching Model for Online Large-scale Recommender System [CIKM 2019][PDF]

  • [Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba [KDD 2018][PDF]

  • [Embedding] Learning and Transferring IDs Representation in E-commerce [KDD 2018] [PDF]

  • [Representations] ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation [AAAI 2018] [PDF]

  • [Representations] Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks [KDD2018][PDF]

  • [exact-K recommendation] Exact-K Recommendation via Maximal Clique Optimization [KDD 2019][PDF]

  • [Explain]A Capsule Network for Recommendation and Explaining What You Like and Dislike [SIGIR2019][PDF][code]

  • [CTR] Privileged Features Distillation for E-Commerce Recommendations [Woodstock ’18][PDF]

  • [CTR] Representation Learning-Assisted Click-Through Rate Prediction [IJCAI 2019] [PDF]

  • [CTR] Deep Session Interest Network for Click-Through Rate Prediction [IJCAI 2019] [PDF]

  • [CTR] Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction] [KDD2019] [PDF] [code]

  • [CTR] Graph Intention Network for Click-through Rate Prediction in Sponsored Search [SIGIR2019] [PDF]

  • [CTR] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction [MLR][PDF]

  • [CTR] Deep Interest Evolution Network for Click-Through Rate Prediction [AAAI2019][PDF]

  • [CTR] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction[KDD2019] [PDF][code]

  • [CTR] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba [PDF]

  • [CVR] Entire Space Multi-Task Model: An E ective Approach for Estimating Post-Click Conversion Rate [SIGIR2018][PDF]

  • [CTR] Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction [SIGIR2019] [PDF] [code]

  • [CTR] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction[Interpretation]

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].