https://github.com/ziyaogeng/rspaper-read
Share Some Recommender System Paper I read.
https://github.com/ziyaogeng/rspaper-read
Last synced: 7 months ago
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Share Some Recommender System Paper I read.
- Host: GitHub
- URL: https://github.com/ziyaogeng/rspaper-read
- Owner: ZiyaoGeng
- Created: 2020-10-08T01:32:58.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-06-26T15:56:08.000Z (over 4 years ago)
- Last Synced: 2025-01-24T09:08:57.159Z (9 months ago)
- Size: 83.7 MB
- Stars: 66
- Watchers: 1
- Forks: 11
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# RSpaper-read
`RSpaper-read`分享一些我读过的经典推荐论文，质量有保障，适合初学者入门。主要分为三部分内容：
1. Review：任何领域入门都少不了综述，推荐的文章包括了基于深度学习的推荐、图学习等；
2. Model：算法模型肯定是推荐领域的重点，按照不同阶段，再细化分为召回（matching）与排序（ranking）：
- matching：召回阶段的模型面临的数据样本是整个物料库，所以它需要在低延时的前提下完成候选物品集的召回给排序阶段；
- ranking：排序阶段区别于召回，要求模型更加复杂，重特征之间的交叉，主要的指标是CTR；
3. Others：推荐中其他的方向或者有趣的内容；
## [Review](./Abstract/review.md)
以下五篇综述都非常适合入门推荐系统：
| | Paper | Published in | Time |
| ---- | :----------------------------------------------------------: | :-------------------: | :--: |
| [1] | Deep Learning for Matching in Search and Recommendation | SIGIR | 2018 |
| [2] | Deep Learning Based Recommender System: A Survey and New Perspectives | ACM Computing Surveys | 2019 |
| [3] | Learning and Reasoning on Graph for Recommendation | CIKM | 2019 |
| [4] | Graph Learning Approaches to Recommender Systems: A Review | IJCAI | 2021 |
| [5] | Sequential Recommender Systems: Challenges, Progress and Prospects | AAAI | 2019 |
## Model
模型按照按照工业界来划分，召回和排序两个大块，由于粗排的文章没有读过，就先不加在里面了。
### [matching](./Abstract/matching.md)
召回阶段，工业界一般会采用多路召回的形式，即使是现在经常使用的基于向量化的召回，也只会作为其中的一路。多路召回的模型中，最常用的就是`ItemCF`（基于实际场景），现在工业界也经常会将其作为一路，毕竟又简单又好用。再往后，最经典的就是`Matrix Factorization`（矩阵分解），召回、排序都可以应用。
| | Paper | Published in | Time |
| :--: | :----------------------------------------------------------: | :----------: | :--: |
| [1] | Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model \| **SVD++** | KDD | 2008 |
| [2] | Matrix Factorization Techniques for Recommender Systems\|**MF** | IEEE | 2009 |
| [3] | Neural network-based Collaborative Filtering \| **NCF** | WWW | 2017 |
再往后，就是基于向量化的召回模型（MF其实也算），**双塔模型**是其中最为通用的架构之一，下面三篇是具有浓厚工业风的文章，业界应用也非常多。
| | Paper | Published in | Time |
| :--: | :----------------------------------------------------------: | :----------: | :--: |
| [4] | Learning Deep Structured Semantic Models for Web Search using Clickthrough Data｜**DSSM** | CIKM | 2013 |
| [5] | Deep Neural Networks for YouTube Recommendations \|**YoutubeDNN** | RecSys | 2016 |
| [6] | Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations | RecSys | 2019 |
目前，也有很多通过用户行为序列来表征用户，即**序列推荐**，这也是我个人的研究方向，文章包含了学术与工业。
| | Paper | Published in | Time |
| :--: | :----------------------------------------------------------: | :----------: | :--: |
| [7] | Factorizing personalized markov chains for next-basket recommendation \| **FMPC** | KDD | 2010 |
| [8] | Learning hierarchical representation model for nextbasket recommendation\|**HRM** | IEEE | 2015 |
| [9] | Translation-based recommendation: A scalable method for modeling sequential behavior \| **TransRec** | IJCAI | 2018 |
| [10] | Session-based Recommendation with Recurrent Neural Networks \| **GRU4Rec** | ICLR | 2016 |
| [11] | Recurrent neural networks with top-k gains for session-based recommendations \| **GRU4Rec+** | WWW | 2017 |
| [12] | Personalized top-n sequential recommendation via convolutional sequence embedding \| **Caser** | ICDM | 2018 |
| [13] | Self-Attentive Sequential Recommendation \| **SASRec** | ICDM | 2018 |
| [14] | STAMP: short-term attention/memory priority model for session-based recommendation｜**STAMP** | KDD | 2018 |
| [15] | Next item recommendation with self-attentive metric learning｜**AttRec** | AAAI | 2019 |
| [16] | BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer｜**BERT4Rec** | CIKM | 2019 |
| [17] | Multi-Interest Network with Dynamic Routing for Recommendation at Tmall \| **MIND** | CIKM | 2019 |
| [18] | FISSA: fusing item similarity models with self-attention networks for sequential recommendation｜**FISSA** | RecSys | 2020 |
| [19] | SSE-PT: Sequential recommendation via personalized transforme｜**SSE-PT** | KDD | 2020 |
| [20] | Time Interval Aware Self-Attention for Sequential Recommendation｜**TiSASRec** | WSDM | 2020 |
| [21] | MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation｜**MEANTIME** | RecSys | 2020 |
| [22] | Controllable Multi-Interest Framework for Recommendation \| **ComiRec** | KDD | 2020 |
| [23] | S3 -Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization｜**S3** | CIKM | 2020 |
| [24] | User BERT: self-supervised user representation learning｜**u-bert** | ICLR | 2021 |
| [25] | Session-Based Recommendation with Graph Neural Networks｜**SR-GNN** | AAAI | 2019 |
| [26] | Sparse-Interest Network for Sequential Recommendation\|**SINE** | WSDM | 2021 |
| [27] | SDM: Sequential Deep Matching Model for Online Large-scale Recommender System\|**SDM** | CIKM | 2019 |
### [ranking](./Abstract/ranking.md)
这里的ranking主要指的是精排部分的模型，
| | Paper | Published in | Time |
| :--: | :----------------------------------------------------------: | :----------: | :--: |
| [1] | Factorization Machines \| **FM** | ICDM | 2010 |
| [2] | Field-aware Factorization Machines for CTR Prediction｜**FFM** | RecSys | 2016 |
| [3] | Wide & Deep Learning for Recommender Systems｜**WDL** | DLRS | 2016 |
| [4] | Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features \| **Deep Crossing** | KDD | 2016 |
| [5] | Product-based Neural Networks for User Response Prediction \| **PNN** | ICDM | 2016 |
| [6] | Deep & Cross Network for Ad Click Predictions \| **DCN** | ADKDD | 2017 |
| [7] | Neural Factorization Machines for Sparse Predictive Analytics \| **NFM** | SIGIR | 2018 |
| [8] | Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks \| **AFM** | IJCAI | 2017 |
| [9] | DeepFM: A Factorization-Machine based Neural Network for CTR Prediction \| **DeepFM** | IJCAI | 2017 |
| [10] | xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems \| **xDeepFM** | KDD | 2018 |
| [11] | Deep Interest Network for Click-Through Rate Prediction \| **DIN** | KDD | 2018 |
| [12] | Behavior Sequence Transformer for E-commerce Recommendation in Alibaba \| **BST** | DLP-KDD | 2019 |
| [13] | Deep Interest Evolution Network for Click-Through Rate Prediction ｜ **DIEN** | AAAI | 2019 |
| [14] | Deep Match to Rank Model for Personalized Click-Through Rate Prediction \| **DMR** | AAAI | 2020 |
序列推荐：
| | Paper | Published in | Time |
| :--: | :----------------------------------------------------------: | :----------: | :--: |
| [15] | Sequential recommendation with user memory networks｜**MANN** | WSDM | 2018 |
多任务：
| | Paper | Published in | Time |
| :--: | :----------------------------------------------------------: | :----------: | :--: |
| [16] | Entire Space Multi-Task Model: An Effective Approach for Estimation Post-Click Conversion Rate \| **ESMM** | SIGIR | 2018 |
| [17] | Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts｜**MMOE** | KDD | 2018 |
## [Others](./Abstract/others.md)
| | Paper | Published in | Time |
| :--: | :----------------------------------------------------------: | :----------: | :--: |
| [1] | Neural Collaborative Filtering vs. Matrix Factorization Revisited | RecSys | 2020 |
树模型，XGB、LGB：
| | Paper | Published in | Time |
| :--: | :--------------------------------------------------------: | :----------: | :--: |
| [2] | XGBoost: A Scalable Tree Boosting System | KDD | 2016 |
| [3] | LightGBM: A Highly Efficient Gradient Boosting Decision Tree | NIPS | 2017 |
Capsules：
| | Paper | Published in | Time |
| :--: | :------------------------------: | :----------: | :--: |
| [4] | Dynamic Routing Between Capsules | NIPS | 2017 |
## Contact Details
作者有一个自己的公众号：**推荐算法的小齿轮**，如果喜欢里面的内容，不妨点个关注。
