My journey in learning about Search & ranking universe, and with this, become better at data science every day
- Evaluation Metrics for Ranking problems: Introduction and Examples
- MRR vs MAP vs NDCG: Rank-Aware Evaluation Metrics And When To Use Them
- Evaluate your Recommendation Engine using NDCG
- Measuring Search Relevance
- Ranking Evaluation Metrics for Recommender Systems
- Explaining Learning to Rank Models with Tree Shap
- Ranking at eBay Part-1 Part-2 Part-3
eBay - eBay Makes Promoted Listings in Search Results More Relevant and Dynamic
eBay - eBay Makes Search More Efficient Through Personalization
eBay - Diversity in Search
eBay2014
- What is Learning To Rank?
- How is search different than other machine learning problems?
- Elasticsearch Learning to Rank documentation
- Learning to Rank journey: the logbook
- What Is a Judgment List?
- Catarina Moreira - Machine Learning
- Admittedly loopy but not entirely absurd—Understanding our Search Relevance Survey
- Learning to Rank using XGBoost
- Learning to Rank 101
- Intuitive explanation of Learning to Rank (and RankNet, LambdaRank and LambdaMART)
- Machine Learning-Powered Search Ranking of Airbnb Experiences
- The Importance of Online Testing in Learning to Rank – Part 1
- Online Testing for Learning To Rank: Interleaving
- Text similarity search with vector fields
ElasticSearch - Siamese BERT-based Model for Web Search Relevance Ranking (Code)
Seznam2021 - SearchSage: Learning Search Query Representations at Pinterest
Pinterest2021 - Search Journey Towards Better Experimentation Practices
Spotify2022 - Searching for Goldilocks
- Thoughts on Search Result Diversity
- Diversity vs. Relevance in Search Systems
- Deriving Search Relevance Judgments from an A/B Test
- Real-time machine learning: challenges and solutions
- An Introduction to Search Quality
- Approaches to field boost tuning with Learning to Rank
- How LambdaMART works - optimizing product ranking goals
- LambdaMART in Depth
- Optimizing Search Engines using Clickthrough Data
- Towards Recency Ranking in Web Search
- On Application of Learning to Rank for E-Commerce Search
- Diversifying Search Results
- Learning Latent Vector Spaces for Product Search
- Yahoo! Learning to Rank Challenge Overview
- On the Usefulness of Query Features for Learning to Rank
- From RankNet to LambdaRank to LambdaMART: An Overview
- Individual Comparisons by Ranking Methods
- Thee whens and hows of learning to rank for web search
- Assignment 2 -Group 95 [
Kaggle - Expedia Hotel Recommendations] - Learning to Rank by Optimizing NDCG Measure
- Ranking Measures and Loss Functions in Learning to Rank
- Debiased Explainable Pairwise Ranking from Implicit Feedback
- Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality
- How to Measure and Mitigate Position Bias
- Learning to Rank with Selection Bias in Personal Search
- Position Bias Estimation for Unbiased Learning to Rank in Personal Search
- Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs
- An experimental comparison of click position-bias models
- Unbiased Learning-to-Rank with Biased Feedback
- Applying Deep Learning To Airbnb Search
Airbnb2018 - Improving Deep Learning For Airbnb Search
Airbnb2020 - Managing Diversity in Airbnb Search
Airbnb2020 - Amazon Search: The joy of ranking products Paper video
Amazon2016 - Real-time Personalization using Embeddings for Search Ranking at Airbnb
Airbnb2018 - Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search
2022 - Multi-Objective Personalized Product Retrieval in Taobao Search
- Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning
2020
- Berlin Buzzwords 17: Doug Turnbull & Jason Kowalewski - We built an Elasticsearch Learning to Rank
- Berlin Buzzwords 2018: Felipe Besson – Learning to Rank journey at GetYourGuide: Our Logbook
- DevConf - Learning "Learning to Rank" - Sophie Watson
- PyData Tel Aviv Meetup: Learning To Rank - Uriel Vinetz]
- Learning to Rank Places [
Slides] - KDD 2020: Hands-onTutorials: Deep Learning for Search and Recommender Systems in Practice
LinkedIn2020 - Airbnb Search Architecture
Airbnb2015 - Measuring and Optimizing Findability in e-commerce Search (MICES 2019)
- Reddit Intelligence Group - Search, Relevance, Discovery, Data Engineering and Anti-Evil
- The Potential for Personalization in Search - Susan Dumais
- Overcoming position and presentation biases in search and recommender system
- TheMovieDB
- Expedia Hotel Recommendations
- Home Depot Product Search Relevance
- Spotify's Worldwide Daily Song Ranking
- Google: Recommendation Systems
- Youtube: Learning to Rank: From Theory to Production - Malvina Josephidou & Diego Ceccarelli, Bloomberg
- Youtube: Learning "Learning to Rank" - Sophie Watson [DevCon 2019]
- Youtube: Learning to rank search results - Byron Voorbach & Jettro Coenradie [DevCon 2018]
- Udemy: Building Recommender Systems with Machine Learning and AI
- Udemy: Recommender Systems and Deep Learning in Python
- Coursera: Recommender Systems
- Stanford University: Mining of Massive Datasets (CS246) - Recommendarion and Dimensionality Reduction Chapters
- Amazon.com Recommendations: Item-to-Item Collaborative Filtering (Paper)
Amazon2003 - Inside our recommender system: Data pipeline execution and monitoring
- On YouTube’s recommendation system
- Stitching together spaces for query-based recommendations
- A Complete Guide To Recommender Systems — Tutorial with Sklearn, Surprise, Keras, Recommenders
- Intro to Recommender Systems: Collaborative Filtering
- Comprehensive Guide To Approximate Nearest Neighbors Algorithms
- Mozrt, a Deep Learning Recommendation System Empowering Walmart Store Associates
Walmart2021 - Near real-time features for near real-time personalization
LinkedIn2022 - Building a Deep Learning Based Retrieval System for Personalized Recommendations
eBay2022 - Real World Recommendation System - Part 1
- [] Real World Recommendation Systems - Part 2 (Training Data Generation)
- [] Real World Recommendation Systems - Part 3 (Modeling)
- Counterfactual Evaluation for Recommendation Systems
- CS246 Book - Stanford University - 3rd edition - Chapter 9 Recommendation Systems
- CS246 Book - Stanford University - 3rd edition - Chapter 11 Dimensionality Reduction
- Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering
- Personalized complementary product recommendation
Amazon2022 - Deep Neural Networks for YouTube Recommendations
YouTube2016
- The ACM Conference Series on Recommender Systems
- An Approach to Data Quality for Netflix Personalization Systems
Netflix2020 - [Meetup com a Globo] Uso da Recomendação e Experimentação na Globo
Globo2022 - Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances
RecSys2021 Tutorial - Building End-to-End Recommender Systems with Nvidia Merlin
Nvidia