RecommenderSystem-Paper

KO | EN

gitlite — search

#hacktoberfest #react #ai #llm #go #golang #rust #deep-learning #docker #nodejs #kubernetes #awesome-list #ios #openai

RecommenderSystem-Paper

(★ 23)

some papers about the recommender system

RecommenderSystem-Paper 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# RecommenderSystem-Paper
papers about the recommender system

## Collaborative Filtering
A [basic paper](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Collaborative%20Filtering/summary/A%20Survey%20of%20Collaborative%20Filtering%20Algorithms%20for%20Social%20Recommender%20Systems.md) about collaborative filtering

The paper that first puts forward the [item-based collaborative filtering](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Collaborative%20Filtering/summary/Item-to-Item%20Collaborative%20Filtering.md)

This paper combines the cf with [implicit feedback](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Collaborative%20Filtering/summary/Collaborative%20Filtering%20for%20Implicit%20Feedback%20Datasets.md)

[A tool for cluster](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Collaborative%20Filtering/summary/Top-N%20Recommender%20System%20via%20Matrix%20Completion.md) in CF

## Matrix Factorization
[Probabilistic Matrix Factorization](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Matrix%20Factorization/summary/Probabilistic%20Matrix%20Factorization.md)

[Social Recommendation Using Probabilistic Matrix Factorization](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Matrix%20Factorization/summary/SoRec%20Social%20Recommendation%20Using%20PMF.md)

## Collaborative Ranking
Combine the [collaborative ranking with social relationships](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Collaborative%20Ranking/summary/Collaborative%20Ranking%20with%20Social%20Relationships%20for%20Top-N%20Recommendations.md)

## Top-N Problem
Combine pairwise ranking loss and pointwise recovery loss for [top-n problem](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Top%20N%20Problem/summary/Improving%20Top-N%20Recommendation%20with%20Heterogeneous%20Loss.md)

Use user graphs and item [graphs for top-n recommendation](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Top%20N%20Problem/summary/Top-N%20rcommendation%20on%20graphs.md)

[entity2rec](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Top%20N%20Problem/summary/entity2rec%20Learning%20User-Item%20Relatedness%20from%20Knowledge%20Graphs%20for%20Top-N%20Item%20Recommendation.md)

## Deep Learning

[Summary](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/%E5%9F%BA%E4%BA%8E%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%9A%84%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E7%BB%BC%E8%BF%B0.pdf)

[Summary 20180327](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/180327.pdf)

### Multilayer Perceptron (MLP)

[NCF](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Neural%20collaborative%20filtering.md)

[NSCR](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Item%20Silk%20Road%20Recommending%20Items%20from%20Information%20Domains%20to%20Social%20Users.md)

[DeepFM](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/DeepFM%20A%20Factorization-Machine%20based%20Neural%20Network%20for%20CTR%20Prediction.md)

[ACF](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Attentive%20Collaborative%20Filtering%20Multimedia%20Recommendation%20with%20Item-%20and%20Component-Level%20Attention.md)
### Autoencoder (AE)

[AutoRec](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/AutoRec%20Autoencoders%20Meet%20Collaborative%20Filtering.md)

[CDAE](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Collaborative%20Denoising%20Auto-Encoders%20for%20Top-N%20Recommender%20Systems.md)

[mDA-CF](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Deep%20Collaborative%20Filtering%20via%20Marginalized%20Denoising%20Auto-encoder.md)

[AutoSVD++](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/AutoSVD%2B%2B%20An%20Efficient%20Hybrid%20Collaborative%20Filtering%20Model%20via%20Contractive%20Auto-encoders.md)

[CDL](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Collaborative%20Deep%20Learning%20for%20Recommender%20Systems.md)

combine with CNN: [CKE](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Collaborative%20Knowledge%20Base%20Embedding%20for%20Recommender%20Systems.md)

### Convolutional Neural Network (CNN)

[Hashtag Recommendation Using Attention-Based Convolutional Neural Network](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Hashtag%20Recommendation%20Using%20Attention-Based%20Convolutional%20Neural%20Network.md)

[DeepCoNN](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Joint%20Deep%20Modeling%20of%20Users%20and%20Items%20Using%20Reviews%20for%20Recommendation.md)

[TransNet](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/TransNets%20Learning%20to%20Transform%20for%20Recommendation.md)

[ConvMF](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Convolutional%20Matrix%20Factorization%20for%20Document%20Context-Aware%20Recommendation.md)

[VPOI](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/What%20Your%20Images%20Reveal%20Exploiting%20Visual%20Contents%20for%20Point-of-Interest%20Recommendation.md)

[Automatic Recommendation Technology for Learning Resources with Convolutional Neural Network](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Automatic%20Recommendation%20Technology%20for%20Learning%20Resources%20with%20Convolutional%20Neural%20Network.md)

[NSPR](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Neural%20Semantic%20Personalized%20Ranking%20for%20item%20coldstart%20recommendation.md)

combine with MLP: [Comparative Deep Learning of Hybrid Representations for Image Recommendations](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Comparative%20Deep%20Learning%20of%20Hybrid%20Representations%20for%20Image%20Recommendations.md)

combine with MLP: [ConTagNet](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/ConTagNet%20Exploiting%20User%20Context%20for%20Image%20Tag%20Recommendation.md)

### Recurrent Neural Network (RNN)

[RRN](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Recurrent%20Recommender%20Networks.md)

[Ask the GRU: Multi-Task Learning for Deep Text Recommendations](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Ask%20the%20GRU%20Multi-Task%20Learning%20for%20Deep%20Text%20Recommendations.md)

combine LSTM with CNN: [Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Hashtag%20Recommendation%20for%20Multimodal%20Microblog%20Using%20Co-Attention%20Network.md)

combine GRU with CNN: [NCN](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Neural%20Citation%20Network%20for%20Context-Aware%20Citation%20Recommendation.md)

### Deep Semantic Similarity Model (DSSM)

[DSPR](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Tag-Aware%20Personalized%20Recommendation%20Using%20a%20Deep-Semantic%20Similarity%20Model%20with%20Negative%20Sampling.md)

[MV-DNN](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/A%20multi-view%20deep%20learning%20approach%20for%20cross%20domain%20user%20modeling%20in%20recommendation%20systems.md)

combine with CNN: [Modeling Interestingness with Deep Neural Networks](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Modeling%20Interestingness%20with%20Deep%20Neural%20Networks.md)

combine with LSTM: [TDSSM](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Deep%20Learning/summary/Multi-Rate%20Deep%20Learning%20for%20Temporal%20Recommendation.md)

### Restricted Boltzmann Machine (RBM)

[RBM-CF]()

## Multitask

[BoWLF&LMLF](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Multitask/summary/Learning%20Distributed%20Representations%20from%20Reviews%20for%20Collaborative%20Filtering.md)

[PSR](https://github.com/chenboability/RecommenderSystem-Paper/blob/master/Multitask/summary/Personalized%20Semantic%20Ranking%20for%20Collaborative%20Recommendation.md)

RecommenderSystem-Paper

RecommenderSystem-Paper 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

RecommenderSystem-Paper

RecommenderSystem-Paper 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README