Merge pull request eugeneyan#100 from eugeneyan/dev

dev

README.md

@@ -108,7 +108,7 @@ P.S., Want a summary of ML advancements? 👉[`ml-surveys`](https://github.com/e
 ## Recommendation
 1. [Amazon.com Recommendations: Item-to-Item Collaborative Filtering](https://ieeexplore.ieee.org/document/1167344) ([Paper](https://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf)) `Amazon`
 2. [Temporal-Contextual Recommendation in Real-Time](https://www.amazon.science/publications/temporal-contextual-recommendation-in-real-time) ([Paper](https://assets.amazon.science/96/71/d1f25754497681133c7aa2b7eb05/temporal-contextual-recommendation-in-real-time.pdf)) `Amazon`
-3. [P-Companion: A Principled Framework for Diversified Complementary Product Recommendation](https://www.amazon.science/publications/p-companion-a-principled-framework-for-diversified-complementary-product-recommendation) ([Paper](https://assets.amazon.science/d5/16/3f7809974a899a11bacdadefdf24/p-companion-a-principled-framework-for-diversified-complementary-product-recommendation.pdf)) `Amazon`
+3. [P-Companion: A Framework for Diversified Complementary Product Recommendation](https://www.amazon.science/publications/p-companion-a-principled-framework-for-diversified-complementary-product-recommendation) ([Paper](https://assets.amazon.science/d5/16/3f7809974a899a11bacdadefdf24/p-companion-a-principled-framework-for-diversified-complementary-product-recommendation.pdf)) `Amazon`
 2. [Recommending Complementary Products in E-Commerce Push Notifications](https://arxiv.org/abs/1707.08113) ([Paper](https://arxiv.org/pdf/1707.08113.pdf)) `Alibaba`
 3. [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/abs/1905.06874) ([Paper](https://arxiv.org/pdf/1905.06874.pdf)) `Alibaba`
 4. [TPG-DNN: A Method for User Intent Prediction with Multi-task Learning](https://arxiv.org/abs/2008.02122) ([Paper](https://arxiv.org/pdf/2008.02122.pdf)) `Alibaba`
@@ -320,7 +320,7 @@ P.S., Want a summary of ML advancements? 👉[`ml-surveys`](https://github.com/e
 9. [Multi-Armed Bandits and the Stitch Fix Experimentation Platform](https://multithreaded.stitchfix.com/blog/2020/08/05/bandits/) `Stitch Fix`
 10. [Modeling Conversion Rates and Saving Millions Using Kaplan-Meier and Gamma Distributions](https://better.engineering/modeling-conversion-rates-and-saving-millions-of-dollars-using-kaplan-meier-and-gamma-distributions/) ([Code](https://github.com/better/convoys)) `Better`
 11. [Computational Causal Inference at Netflix](https://netflixtechblog.com/computational-causal-inference-at-netflix-293591691c62) ([Paper](https://arxiv.org/pdf/2007.10979.pdf)) `Netflix`
-12. [Key Challenges with Quasi Experiments at Netflix](httpss://netflixtechblog.com/key-challenges-with-quasi-experiments-at-netflix-89b4f234b852) `Netflix`
+12. [Key Challenges with Quasi Experiments at Netflix](https://netflixtechblog.com/key-challenges-with-quasi-experiments-at-netflix-89b4f234b852) `Netflix`
 13. [Constrained Bayesian Optimization with Noisy Experiments](https://research.fb.com/publications/constrained-bayesian-optimization-with-noisy-experiments/) ([Paper](https://arxiv.org/pdf/1706.07094.pdf)) `Facebook`
 14. [Supporting Rapid Product Iteration with an Experimentation Analysis Platform](https://doordash.engineering/2020/09/09/experimentation-analysis-platform-mvp/) `Curie`
 15. [Our Evolution Towards T-REX: The Prehistory of Experimentation Infrastructure at LinkedIn](https://engineering.linkedin.com/blog/2020/our-evolution-towards-t-rex--the-prehistory-of-experimentation-i) `LinkedIn`