# Reco-papers

**Repository Path**: Ivanmax/Reco-papers

## Basic Information

- **Project Name**: Reco-papers
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-06-27
- **Last Updated**: 2026-06-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 推荐系统论文、学习资料、业界分享
动态更新工作中实现或者阅读过的推荐系统相关论文、学习资料和业界分享，作为自己工作的总结，也希望能为推荐系统相关行业的同学带来便利。
所有资料均来自于互联网，如有侵权，请联系王喆。同时欢迎对推荐系统感兴趣的同学与我讨论相关问题，我的联系方式如下：
* Email: wzhe06@gmail.com
* LinkedIn: [王喆的LinkedIn](https://www.linkedin.com/in/zhe-wang-profile/)
* 知乎私信: [王喆的知乎](https://www.zhihu.com/people/wang-zhe-58)

**其他相关资源**
* [计算广告相关论文和资源列表](https://github.com/wzhe06/Ad-papers) <br />
* [张伟楠的RTB Papers列表](https://github.com/wnzhang/rtb-papers)<br />
* [搜广推业界实践文章](https://github.com/Doragd/Algorithm-Practice-in-Industry) <br />
* [Honglei Zhang的推荐系统论文列表](https://github.com/hongleizhang/RSPapers)


## 目录

### Retrieval and Rerank
* [[Distillation] Distillation Based Multi-task Learning- A Candidate Generation Model for Improving Reading Duration](https://github.com/wzhe06/Reco-papers/blob/master/Retrieval%20and%20Rerank/%5BDistillation%5D%20Distillation%20Based%20Multi-task%20Learning-%20A%20Candidate%20Generation%20Model%20for%20Improving%20Reading%20Duration.pdf) <br />
* [[PRM] Personalized Re-ranking for Recommendation](https://github.com/wzhe06/Reco-papers/blob/master/Retrieval%20and%20Rerank/%5BPRM%5D%20Personalized%20Re-ranking%20for%20Recommendation.pdf) <br />
* [[COLD] Towards the Next Generation of Pre-ranking System](https://github.com/wzhe06/Reco-papers/blob/master/Retrieval%20and%20Rerank/%5BCOLD%5D%20Towards%20the%20Next%20Generation%20of%20Pre-ranking%20System.pdf) <br />
* [[Seq2Slate] Re-ranking and Slate Optimization with RNNs](https://github.com/wzhe06/Reco-papers/blob/master/Retrieval%20and%20Rerank/%5BSeq2Slate%5D%20Re-ranking%20and%20Slate%20Optimization%20with%20RNNs.pdf) <br />
* [[Hulu Diversity] Fast Greedy Map Inference for Determinantal Point Processes to Improve Recommendation Diversity](https://github.com/wzhe06/Reco-papers/blob/master/Retrieval%20and%20Rerank/%5BHulu%20Diversity%5D%20Fast%20Greedy%20Map%20Inference%20for%20Determinantal%20Point%20Processes%20to%20Improve%20Recommendation%20Diversity.pdf) <br />
* [[TDM] Learning Tree-based Deep Model for Recommender Systems](https://github.com/wzhe06/Reco-papers/blob/master/Retrieval%20and%20Rerank/%5BTDM%5D%20Learning%20Tree-based%20Deep%20Model%20for%20Recommender%20Systems.pdf) <br />
* [[LTR] From RankNet to LambdaRank to LambdaMART- An Overview](https://github.com/wzhe06/Reco-papers/blob/master/Retrieval%20and%20Rerank/%5BLTR%5D%20From%20RankNet%20to%20LambdaRank%20to%20LambdaMART-%20An%20Overview.pdf) <br />
* [[AirBnb Rerank] Managing Diversity in Airbnb Search](https://github.com/wzhe06/Reco-papers/blob/master/Retrieval%20and%20Rerank/%5BAirBnb%20Rerank%5D%20Managing%20Diversity%20in%20Airbnb%20Search.pdf) <br />
* [[Deep Retrieval] Learning a Retrievable Structure for Large-scale Recommendations](https://github.com/wzhe06/Reco-papers/blob/master/Retrieval%20and%20Rerank/%5BDeep%20Retrieval%5D%20Learning%20a%20Retrievable%20Structure%20for%20Large-scale%20Recommendations.pdf) <br />

### Deep Learning Recommender System
* [[DCN] Deep & Cross Network for Ad Click Predictions (Stanford 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDCN%5D%20Deep%20%26%20Cross%20Network%20for%20Ad%20Click%20Predictions%20%28Stanford%202017%29.pdf) <br />
* [[Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDeep%20Crossing%5D%20Deep%20Crossing%20-%20Web-Scale%20Modeling%20without%20Manually%20Crafted%20Combinatorial%20Features%20%28Microsoft%202016%29.pdf) <br />
* [[DIN] Deep Interest Network for Click-Through Rate Prediction (Alibaba 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDIN%5D%20Deep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction%20%28Alibaba%202018%29.pdf) <br />
* [[DL Recsys Intro] Deep Learning based Recommender System- A Survey and New Perspectives (UNSW 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDL%20Recsys%20Intro%5D%20Deep%20Learning%20based%20Recommender%20System-%20A%20Survey%20and%20New%20Perspectives%20%28UNSW%202018%29.pdf) <br />
* [[PinnerFormer] Sequence Modeling for User Representation at Pinterest](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BPinnerFormer%5D%20Sequence%20Modeling%20for%20User%20Representation%20at%20Pinterest.pdf) <br />
* [[xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems (USTC 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BxDeepFM%5D%20xDeepFM%20-%20Combining%20Explicit%20and%20Implicit%20Feature%20Interactions%20for%20Recommender%20Systems%20%28USTC%202018%29.pdf) <br />
* [[Image CTR] Image Matters - Visually modeling user behaviors using Advanced Model Server (Alibaba 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BImage%20CTR%5D%20Image%20Matters%20-%20Visually%20modeling%20user%20behaviors%20using%20Advanced%20Model%20Server%20%28Alibaba%202018%29.pdf) <br />
* [[CDL] Collaborative Deep Learning for Recommender Systems (HKUST, 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BCDL%5D%20Collaborative%20Deep%20Learning%20for%20Recommender%20Systems%20%28HKUST%2C%202015%29.pdf) <br />
* [[DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (Microsoft 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDSSM%20in%20Recsys%5D%20A%20Multi-View%20Deep%20Learning%20Approach%20for%20Cross%20Domain%20User%20Modeling%20in%20Recommendation%20Systems%20%28Microsoft%202015%29.pdf) <br />
* [[AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BAFM%5D%20Attentional%20Factorization%20Machines%20-%20Learning%20the%20Weight%20of%20Feature%20Interactions%20via%20Attention%20Networks%20%28ZJU%202017%29.pdf) <br />
* [[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction (Alibaba 2019)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDIEN%5D%20Deep%20Interest%20Evolution%20Network%20for%20Click-Through%20Rate%20Prediction%20%28Alibaba%202019%29.pdf) <br />
* [[Wide&Deep] Wide & Deep Learning for Recommender Systems (Google 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BWide%26Deep%5D%20Wide%20%26%20Deep%20Learning%20for%20Recommender%20Systems%20%28Google%202016%29.pdf) <br />
* [[DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDSSM%5D%20Learning%20Deep%20Structured%20Semantic%20Models%20for%20Web%20Search%20using%20Clickthrough%20Data%20%28UIUC%202013%29.pdf) <br />
* [[NCF] Neural Collaborative Filtering (NUS 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BNCF%5D%20Neural%20Collaborative%20Filtering%20%28NUS%202017%29.pdf) <br />
* [[FNN] Deep Learning over Multi-field Categorical Data (UCL 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BFNN%5D%20Deep%20Learning%20over%20Multi-field%20Categorical%20Data%20%28UCL%202016%29.pdf) <br />
* [[DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDeepFM%5D%20A%20Factorization-Machine%20based%20Neural%20Network%20for%20CTR%20Prediction%20%28HIT-Huawei%202017%29.pdf) <br />
* [[NFM] Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BNFM%5D%20Neural%20Factorization%20Machines%20for%20Sparse%20Predictive%20Analytics%20%28NUS%202017%29.pdf) <br />
* [[Latent Cross] Latent Cross- Making Use of Context in Recurrent Recommender Systems (Google 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BLatent%20Cross%5D%20Latent%20Cross-%20Making%20Use%20of%20Context%20in%20Recurrent%20Recommender%20Systems%20%28Google%202018%29.pdf) <br />
* [[TransAct] Transformer-based Real-time User Action Model for Recommendation at Pinterest](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BTransAct%5D%20Transformer-based%20Real-time%20User%20Action%20Model%20for%20Recommendation%20at%20Pinterest.pdf) <br />

### Embedding
* [[Negative Sampling] Word2vec Explained Negative-Sampling Word-Embedding Method (2014)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BNegative%20Sampling%5D%20Word2vec%20Explained%20Negative-Sampling%20Word-Embedding%20Method%20%282014%29.pdf) <br />
* [[SDNE] Structural Deep Network Embedding (THU 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BSDNE%5D%20Structural%20Deep%20Network%20Embedding%20%28THU%202016%29.pdf) <br />
* [[Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BItem2Vec%5D%20Item2Vec-Neural%20Item%20Embedding%20for%20Collaborative%20Filtering%20%28Microsoft%202016%29.pdf) <br />
* [[Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BWord2Vec%5D%20Distributed%20Representations%20of%20Words%20and%20Phrases%20and%20their%20Compositionality%20%28Google%202013%29.pdf) <br />
* [[LSH] Locality-Sensitive Hashing for Finding Nearest Neighbors (IEEE 2008)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BLSH%5D%20Locality-Sensitive%20Hashing%20for%20Finding%20Nearest%20Neighbors%20%28IEEE%202008%29.pdf) <br />
* [[Word2Vec] Word2vec Parameter Learning Explained (UMich 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BWord2Vec%5D%20Word2vec%20Parameter%20Learning%20Explained%20%28UMich%202016%29.pdf) <br />
* [[GraphSAGE]Inductive Representation Learning on Large Graphs](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BGraphSAGE%5DInductive%20Representation%20Learning%20on%20Large%20Graphs.pdf) <br />
* [[Node2vec] Node2vec - Scalable Feature Learning for Networks (Stanford 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BNode2vec%5D%20Node2vec%20-%20Scalable%20Feature%20Learning%20for%20Networks%20%28Stanford%202016%29.pdf) <br />
* [[Graph Embedding] DeepWalk- Online Learning of Social Representations (SBU 2014)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BGraph%20Embedding%5D%20DeepWalk-%20Online%20Learning%20of%20Social%20Representations%20%28SBU%202014%29.pdf) <br />
* [[RippleNet] Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BRippleNet%5D%20Propagating%20User%20Preferences%20on%20the%20Knowledge%20Graph%20for%20Recommender%20Systems.pdf) <br />
* [[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BAirbnb%20Embedding%5D%20Real-time%20Personalization%20using%20Embeddings%20for%20Search%20Ranking%20at%20Airbnb%20%28Airbnb%202018%29.pdf) <br />
* [[Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Alibaba 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BAlibaba%20Embedding%5D%20Billion-scale%20Commodity%20Embedding%20for%20E-commerce%20Recommendation%20in%20Alibaba%20%28Alibaba%202018%29.pdf) <br />
* [[KGAT] Knowledge Graph Attention Network for Recommendation](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BKGAT%5D%20Knowledge%20Graph%20Attention%20Network%20for%20Recommendation.pdf) <br />
* [[Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BWord2Vec%5D%20Efficient%20Estimation%20of%20Word%20Representations%20in%20Vector%20Space%20%28Google%202013%29.pdf) <br />
* [[Explainable RS]Fairness-aware Explainable Recommendation over Knowledge Graphs](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BExplainable%20RS%5DFairness-aware%20Explainable%20Recommendation%20over%20Knowledge%20Graphs.pdf) <br />
* [[LINE] LINE - Large-scale Information Network Embedding (MSRA 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BLINE%5D%20LINE%20-%20Large-scale%20Information%20Network%20Embedding%20%28MSRA%202015%29.pdf) <br />

### Famous Machine Learning Papers
* [[Attention] Attention is All You Need](https://github.com/wzhe06/Reco-papers/blob/master/Famous%20Machine%20Learning%20Papers/%5BAttention%5D%20Attention%20is%20All%20You%20Need.pdf) <br />
* [[RNN] Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (UofM 2014)](https://github.com/wzhe06/Reco-papers/blob/master/Famous%20Machine%20Learning%20Papers/%5BRNN%5D%20Learning%20Phrase%20Representations%20using%20RNN%20Encoder%E2%80%93Decoder%20for%20Statistical%20Machine%20Translation%20%28UofM%202014%29.pdf) <br />
* [[CNN] ImageNet Classification with Deep Convolutional Neural Networks (UofT 2012)](https://github.com/wzhe06/Reco-papers/blob/master/Famous%20Machine%20Learning%20Papers/%5BCNN%5D%20ImageNet%20Classification%20with%20Deep%20Convolutional%20Neural%20Networks%20%28UofT%202012%29.pdf) <br />

### Multi-Task
* [[ESMM] Entire Space Multi-task Model- An Effective Approach for Estimating Post-click Conversion Rate](https://github.com/wzhe06/Reco-papers/blob/master/Multi-Task/%5BESMM%5D%20Entire%20Space%20Multi-task%20Model-%20An%20Effective%20Approach%20for%20Estimating%20Post-click%20Conversion%20Rate.pdf) <br />
* [[MMoE] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://github.com/wzhe06/Reco-papers/blob/master/Multi-Task/%5BMMoE%5D%20Modeling%20Task%20Relationships%20in%20Multi-task%20Learning%20with%20Multi-gate%20Mixture-of-Experts.pdf) <br />
* [[PLE] Progressive Layered Extraction (PLE)- A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://github.com/wzhe06/Reco-papers/blob/master/Multi-Task/%5BPLE%5D%20Progressive%20Layered%20Extraction%20%28PLE%29-%20A%20Novel%20Multi-Task%20Learning%20%28MTL%29%20Model%20for%20Personalized%20Recommendations.pdf) <br />

### Feature Data and Infra
* [[Privacy] Privacy-preserving News Recommendation Model Learning](https://github.com/wzhe06/Reco-papers/blob/master/Feature%20Data%20and%20Infra/%5BPrivacy%5D%20Privacy-preserving%20News%20Recommendation%20Model%20Learning.pdf) <br />
* [[EdgeRec] Recommender System on Edge in Mobile Taobao](https://github.com/wzhe06/Reco-papers/blob/master/Feature%20Data%20and%20Infra/%5BEdgeRec%5D%20Recommender%20System%20on%20Edge%20in%20Mobile%20Taobao.pdf) <br />
* [[MMKGs] Multi-modal Knowledge Graphs for Recommender Systems](https://github.com/wzhe06/Reco-papers/blob/master/Feature%20Data%20and%20Infra/%5BMMKGs%5D%20Multi-modal%20Knowledge%20Graphs%20for%20Recommender%20Systems.pdf) <br />
* [[Delayed Feedback] Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-time Sampling](https://github.com/wzhe06/Reco-papers/blob/master/Feature%20Data%20and%20Infra/%5BDelayed%20Feedback%5D%20Capturing%20Delayed%20Feedback%20in%20Conversion%20Rate%20Prediction%20via%20Elapsed-time%20Sampling.pdf) <br />
* [[Delayed Feedback] Handling Many Conversions Per Click in Modeling Delayed Feedback](https://github.com/wzhe06/Reco-papers/blob/master/Feature%20Data%20and%20Infra/%5BDelayed%20Feedback%5D%20Handling%20Many%20Conversions%20Per%20Click%20in%20Modeling%20Delayed%20Feedback.pdf) <br />
* [[ViLBERT] Pretraining Task-agnostic Visiolinguistic Representations for Vision-and-language Tasks](https://github.com/wzhe06/Reco-papers/blob/master/Feature%20Data%20and%20Infra/%5BViLBERT%5D%20Pretraining%20Task-agnostic%20Visiolinguistic%20Representations%20for%20Vision-and-language%20Tasks.pdf) <br />
* [[MM-Rec] Multimodal News Recommendation](https://github.com/wzhe06/Reco-papers/blob/master/Feature%20Data%20and%20Infra/%5BMM-Rec%5D%20Multimodal%20News%20Recommendation.pdf) <br />

### Classic Recommender System
* [[MF] Matrix Factorization Techniques for Recommender Systems (Yahoo 2009)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BMF%5D%20Matrix%20Factorization%20Techniques%20for%20Recommender%20Systems%20%28Yahoo%202009%29.pdf) <br />
* [[Earliest CF] Using Collaborative Filtering to Weave an Information Tapestry (PARC 1992)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BEarliest%20CF%5D%20Using%20Collaborative%20Filtering%20to%20Weave%20an%20Information%20Tapestry%20%28PARC%201992%29.pdf) <br />
* [[Recsys Intro] Recommender Systems Handbook (FRicci 2011)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BRecsys%20Intro%5D%20Recommender%20Systems%20Handbook%20%28FRicci%202011%29.pdf) <br />
* [[Recsys Intro slides] Recommender Systems An introduction (DJannach 2014)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BRecsys%20Intro%20slides%5D%20Recommender%20Systems%20An%20introduction%20%28DJannach%202014%29.pdf) <br />
* [[CF] Amazon Recommendations Item-to-Item Collaborative Filtering (Amazon 2003)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BCF%5D%20Amazon%20Recommendations%20Item-to-Item%20Collaborative%20Filtering%20%28Amazon%202003%29.pdf) <br />
* [[ItemCF] Item-Based Collaborative Filtering Recommendation Algorithms (UMN 2001)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BItemCF%5D%20Item-Based%20Collaborative%20Filtering%20Recommendation%20Algorithms%20%28UMN%202001%29.pdf) <br />
* [[Bilinear] Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models (Yahoo 2009)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BBilinear%5D%20Personalized%20Recommendation%20on%20Dynamic%20Content%20Using%20Predictive%20Bilinear%20Models%20%28Yahoo%202009%29.pdf) <br />

### LLM Recommender System
* [[Once] Boosting Content-based Recommendation with Both Open-and Closed-source Large Language Models](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BOnce%5D%20Boosting%20Content-based%20Recommendation%20with%20Both%20Open-and%20Closed-source%20Large%20Language%20Models.pdf) <br />
* [[PALR] Personalization Aware LLMs for Recommendation](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BPALR%5D%20Personalization%20Aware%20LLMs%20for%20Recommendation.pdf) <br />
* [[Onesearch] A preliminary exploration of the unified end-to-end generative framework for e-commerce search](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BOnesearch%5D%20A%20preliminary%20exploration%20of%20the%20unified%20end-to-end%20generative%20framework%20for%20e-commerce%20search.pdf) <br />
* [[NoteLLM] A Retrievable Large Language Model for Note Recommendation](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BNoteLLM%5D%20A%20Retrievable%20Large%20Language%20Model%20for%20Note%20Recommendation.pdf) <br />
* [[PMG] Personalized Multimodal Generation with Large Language Models](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BPMG%5D%20Personalized%20Multimodal%20Generation%20with%20Large%20Language%20Models.pdf) <br />
* [[MTGR] Industrial-Scale Generative Recommendation Framework in Meituan](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BMTGR%5D%20Industrial-Scale%20Generative%20Recommendation%20Framework%20in%20Meituan.pdf) <br />
* [[MoRecl] Where to Go Next for Recommender Systems? Id-vs. Modality-based Recommender Models](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BMoRecl%5D%20Where%20to%20Go%20Next%20for%20Recommender%20Systems%3F%20Id-vs.%20Modality-based%20Recommender%20Models.pdf) <br />
* [[GR] Generative Recommendation- Towards Next-generation Recommender Paradigm](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BGR%5D%20Generative%20Recommendation-%20Towards%20Next-generation%20Recommender%20Paradigm.pdf) <br />
* [[MetaGR] Actions Speak Louder than Words- Trillion-Parameter Sequential Transducers for Generative Recommendations](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BMetaGR%5D%20Actions%20Speak%20Louder%20than%20Words-%20Trillion-Parameter%20Sequential%20Transducers%20for%20Generative%20Recommendations.pdf) <br />
* [[OneRec] Unifying Retrieve and Rank with Generative Recommender and Preference Alignment](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BOneRec%5D%20Unifying%20Retrieve%20and%20Rank%20with%20Generative%20Recommender%20and%20Preference%20Alignment.pdf) <br />
* [[ClickPrompt] CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BClickPrompt%5D%20CTR%20Models%20are%20Strong%20Prompt%20Generators%20for%20Adapting%20Language%20Models%20to%20CTR%20Prediction.pdf) <br />
* [[Tiger] Recommender Systems with Generative Retrieval](https://github.com/wzhe06/Reco-papers/blob/master/LLM%20Recommender%20System/%5BTiger%5D%20Recommender%20Systems%20with%20Generative%20Retrieval.pdf) <br />

### Evaluation
* [[EE Evaluation Intro] Offline Evaluation and Optimization for Interactive Systems (Microsoft 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BEE%20Evaluation%20Intro%5D%20Offline%C2%A0Evaluation%C2%A0and%C2%A0Optimization%20for%C2%A0Interactive%C2%A0Systems%20%28Microsoft%202015%29.pdf) <br />
* [[Bootstrapped Replay] Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques (Ulille 2014)](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BBootstrapped%20Replay%5D%20Improving%20offline%20evaluation%20of%20contextual%20bandit%20algorithms%20via%20bootstrapping%20techniques%20%28Ulille%202014%29.pdf) <br />
* [[InterLeaving] Large-Scale Validation and Analysis of Interleaved Search Evaluation (Yahoo 2012)](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BInterLeaving%5D%20Large-Scale%20Validation%20and%20Analysis%20of%20Interleaved%20Search%20Evaluation%20%28Yahoo%202012%29.pdf) <br />
* [[RecSim] A Configurable Simulation Platform for Recommender Systems](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BRecSim%5D%20A%20Configurable%20Simulation%20Platform%20for%20Recommender%20Systems.pdf) <br />
* [[Replay] Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms (Yahoo 2012)](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BReplay%5D%20Unbiased%20Offline%20Evaluation%20of%20Contextual-bandit-based%20News%20Article%20Recommendation%20Algorithms%20%28Yahoo%202012%29.pdf) <br />
* [[Eval Agent] User Behavior Simulation with Large Language Model based Agents](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BEval%20Agent%5D%20User%20Behavior%20Simulation%20with%20Large%20Language%20Model%20based%20Agents.pdf) <br />
* [[Classic Metrics] A Survey of Accuracy Evaluation Metrics of Recommendation Tasks (Microsoft 2009)](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BClassic%20Metrics%5D%20A%20Survey%20of%20Accuracy%20Evaluation%20Metrics%20of%20Recommendation%20Tasks%20%28Microsoft%202009%29.pdf) <br />

### Reinforcement Learning in Reco
* [[Active Learning] Active Learning in Collaborative Filtering Recommender Systems(UNIBZ 2014)](https://github.com/wzhe06/Reco-papers/blob/master/Reinforcement%20Learning%20in%20Reco/%5BActive%20Learning%5D%20Active%20Learning%20in%20Collaborative%20Filtering%20Recommender%20Systems%28UNIBZ%202014%29.pdf) <br />
* [[RL Music] Exploration in Interactive Personalized Music Recommendation- A Reinforcement Learning Approach (NUS 2013)](https://github.com/wzhe06/Reco-papers/blob/master/Reinforcement%20Learning%20in%20Reco/%5BRL%20Music%5D%20Exploration%20in%20Interactive%20Personalized%20Music%20Recommendation-%20A%20Reinforcement%20Learning%20Approach%20%28NUS%202013%29.pdf) <br />
* [[Active Learning] A survey of active learning in collaborative filtering recommender systems (POLIMI 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Reinforcement%20Learning%20in%20Reco/%5BActive%20Learning%5D%20A%20survey%20of%20active%20learning%20in%20collaborative%20filtering%20recommender%20systems%20%28POLIMI%202016%29.pdf) <br />
* [[DRN] A Deep Reinforcement Learning Framework for News Recommendation (MSRA 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Reinforcement%20Learning%20in%20Reco/%5BDRN%5D%20A%20Deep%20Reinforcement%20Learning%20Framework%20for%20News%20Recommendation%20%28MSRA%202018%29.pdf) <br />

### Industry Recommender System
* [[Pinterest] Personalized content blending In the Pinterest home feed (Pinterest 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BPinterest%5D%20Personalized%20content%20blending%20In%20the%20Pinterest%20home%20feed%20%28Pinterest%202016%29.pdf) <br />
* [[Pinterest] Graph Convolutional Neural Networks for Web-Scale Recommender Systems (Pinterest 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BPinterest%5D%20Graph%20Convolutional%20Neural%20Networks%20for%20Web-Scale%20Recommender%20Systems%20%28Pinterest%202018%29.pdf) <br />
* [[Airbnb] Search Ranking and Personalization at Airbnb Slides (Airbnb 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BAirbnb%5D%20Search%20Ranking%20and%20Personalization%20at%20Airbnb%20Slides%20%28Airbnb%202018%29.pdf) <br />
* [[Baidu slides] DNN in Baidu Ads (Baidu 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BBaidu%20slides%5D%20DNN%20in%20Baidu%20Ads%20%28Baidu%202017%29.pdf) <br />
* [[Quora] Building a Machine Learning Platform at Quora (Quora 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BQuora%5D%20Building%20a%20Machine%20Learning%20Platform%20at%20Quora%20%28Quora%202016%29.pdf) <br />
* [[Airbnb] Optimizing Airbnb Search Journey with Multi-task Learning](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BAirbnb%5D%20Optimizing%20Airbnb%20Search%20Journey%20with%20Multi-task%20Learning.pdf) <br />
* [[Alibaba] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for CTR Prediction](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BAlibaba%5D%20Search-based%20User%20Interest%20Modeling%20with%20Lifelong%20Sequential%20Behavior%20Data%20for%20CTR%20Prediction.pdf) <br />
* [[Alibaba Star] One Model to Serve All- Star Topology Adaptive Recommender for Multi-domain CTR Prediction](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BAlibaba%20Star%5D%20One%20Model%20to%20Serve%20All-%20Star%20Topology%20Adaptive%20Recommender%20for%20Multi-domain%20CTR%20Prediction.pdf) <br />
* [[Netflix] The Netflix Recommender System- Algorithms, Business Value, and Innovation (Netflix 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BNetflix%5D%20The%20Netflix%20Recommender%20System-%20Algorithms%2C%20Business%20Value%2C%20and%20Innovation%20%28Netflix%202015%29.pdf) <br />
* [[Youtube] Deep Neural Networks for YouTube Recommendations (Youtube 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BYoutube%5D%20Deep%20Neural%20Networks%20for%20YouTube%20Recommendations%20%28Youtube%202016%29.pdf) <br />
* [[Alibaba] Capturing Conversion Rate Fluctuation During Sales Promotions- A Novel Historical Data Reuse Approach](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BAlibaba%5D%20Capturing%20Conversion%20Rate%20Fluctuation%20During%20Sales%20Promotions-%20A%20Novel%20Historical%20Data%20Reuse%20Approach.pdf) <br />
* [[Airbnb] Applying Deep Learning To Airbnb Search (Airbnb 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BAirbnb%5D%20Applying%20Deep%20Learning%20To%20Airbnb%20Search%20%28Airbnb%202018%29.pdf) <br />
* [[Alibaba] Image Matters- Visually Modeling User Behaviors Using Advanced Model Server](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BAlibaba%5D%20Image%20Matters-%20Visually%20Modeling%20User%20Behaviors%20Using%20Advanced%20Model%20Server.pdf) <br />

### Exploration and Exploitation
* [[EE in Ads] Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments (UMich 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BEE%20in%20Ads%5D%20Customer%20Acquisition%20via%20Display%20Advertising%20Using%20MultiArmed%20Bandit%20Experiments%20%28UMich%202015%29.pdf) <br />
* [[EE in Ads] Exploitation and Exploration in a Performance based Contextual Advertising System (Yahoo 2010)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BEE%20in%20Ads%5D%20Exploitation%20and%20Exploration%20in%20a%20Performance%20based%20Contextual%20Advertising%20System%20%28Yahoo%202010%29.pdf) <br />
* [[EE in AlphaGo]Mastering the game of Go with deep neural networks and tree search (Deepmind 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BEE%20in%20AlphaGo%5DMastering%20the%20game%20of%20Go%20with%20deep%20neural%20networks%20and%20tree%20search%20%28Deepmind%202016%29.pdf) <br />
* [[UCB1] Bandit Algorithms Continued - UCB1 (Noel Welsh 2010)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BUCB1%5D%20Bandit%20Algorithms%20Continued%20-%20UCB1%20%28Noel%20Welsh%202010%29.pdf) <br />
* [[Spotify] Explore, Exploit, and Explain- Personalizing Explainable Recommendations with Bandits (Spotify 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BSpotify%5D%20Explore%2C%20Exploit%2C%20and%20Explain-%20Personalizing%20Explainable%20Recommendations%20with%20Bandits%20%28Spotify%202018%29.pdf) <br />
* [[TS Intro] Thompson Sampling Slides (Berkeley 2010)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BTS%20Intro%5D%20Thompson%20Sampling%20Slides%20%28Berkeley%202010%29.pdf) <br />
* [[Thompson Sampling] An Empirical Evaluation of Thompson Sampling (Yahoo 2011)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BThompson%20Sampling%5D%20An%20Empirical%20Evaluation%20of%20Thompson%20Sampling%20%28Yahoo%202011%29.pdf) <br />
* [[UCT] Exploration exploitation in Go UCT for Monte-Carlo Go (UPSUD 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BUCT%5D%20Exploration%20exploitation%20in%20Go%20UCT%20for%20Monte-Carlo%20Go%20%28UPSUD%202016%29.pdf) <br />
* [[LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation (Yahoo 2010)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BLinUCB%5D%20A%20Contextual-Bandit%20Approach%20to%20Personalized%20News%20Article%20Recommendation%20%28Yahoo%202010%29.pdf) <br />
* [[RF in MAB]Random Forest for the Contextual Bandit Problem (Orange 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BRF%20in%20MAB%5DRandom%20Forest%20for%20the%20Contextual%20Bandit%20Problem%20%28Orange%202016%29.pdf) <br />
* [[EE Intro] Exploration and Exploitation Problem Introduction by Wang Zhe (Hulu 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BEE%20Intro%5D%20Exploration%20and%20Exploitation%20Problem%20Introduction%20by%20Wang%20Zhe%20%28Hulu%202017%29.pdf) <br />

### Cold Start and Debias
* [[RS Bias] Bias and Debias in Recommender System- A Survey and Future Directions](https://github.com/wzhe06/Reco-papers/blob/master/Cold%20Start%20and%20Debias/%5BRS%20Bias%5D%20Bias%20and%20Debias%20in%20Recommender%20System-%20A%20Survey%20and%20Future%20Directions.pdf) <br />
* [[Meta Emb]Warm Up Cold-start Advertisements- Improving CTR Predictions via Learning to Learn ID Embeddings](https://github.com/wzhe06/Reco-papers/blob/master/Cold%20Start%20and%20Debias/%5BMeta%20Emb%5DWarm%20Up%20Cold-start%20Advertisements-%20Improving%20CTR%20Predictions%20via%20Learning%20to%20Learn%20ID%20Embeddings.pdf) <br />
* [[PAL] A Position-Bias Aware Learning Framework for CTR Prediction in Live Recommender Systems](https://github.com/wzhe06/Reco-papers/blob/master/Cold%20Start%20and%20Debias/%5BPAL%5D%20A%20Position-Bias%20Aware%20Learning%20Framework%20for%20CTR%20Prediction%20in%20Live%20Recommender%20Systems.pdf) <br />
* [[DICE] Disentangling User Interest and Conformity for Recommendation with Causal Embedding](https://github.com/wzhe06/Reco-papers/blob/master/Cold%20Start%20and%20Debias/%5BDICE%5D%20Disentangling%20User%20Interest%20and%20Conformity%20for%20Recommendation%20with%20Causal%20Embedding.pdf) <br />