Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Classic papers and resources on recommendation
| Date | Stars |
|---|---|
| 2026-07-30 | 3567 |
| 2026-08-10 | 3572 |
| 2026-08-18 | 3570 |
| 2026-08-25 | 3570 |
| 2026-08-27 | 3568 |
| 2026-08-29 | 3567 |
| 2026-08-31 | 3568 |
| 2026-09-04 | 3569 |
| 2026-09-06 | 3568 |
| 2026-09-09 | 3569 |
| 2026-09-13 | 3570 |
| 2026-09-18 | 3571 |
| 2026-09-19 | 3570 |
| 2026-09-21 | 3571 |
| 2026-09-22 | 3571 |
| 2026-09-27 | 3571 |
| 2026-09-28 | 3570 |
| 2026-09-29 | 3571 |
| 2026-10-02 | 3573 |
| 2026-10-05 | 3572 |
| 2026-10-10 | 3574 |
| 2026-10-12 | 3575 |
| 2026-10-13 | 3576 |
| 2026-10-19 | 3577 |
| 2026-10-21 | 3577 |
| 2026-10-22 | 3578 |
| 2026-10-31 | 3578 |
| 2026-11-04 | 3578 |
Today
— stars today
This week
— stars this week
This month
+6 stars this month
Momentum
0.0
growth rate 0.00%/day
# 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: [email protected] * 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/wzhe
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Wang Zhe
21
6
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1527b20612ee485b, topic:deep-learning
matched fp:1527b20612ee485b, topic:reinforcement-learning