Click Models

WIP

Papers

• Comparing Click Logs and Editorial Labels for Training Query Rewriting

  • Clicks Normalized By Expected Clicks - COEC

• On Application of Learning to Rank for E-Commerce Search

  • Label generation [3 EXPERIMENT DESIGN]  

Articles

• Click Models for Web Search - Home

• Building a click model: From idea to practice - ScienceDirectScienceDirect

• Intent Based Relevance Estimation from Click Logs

• https://zhangyuc.github.io/

  • Incorporating Post-Click Behaviors into a Click Model
  • Explore Click Models for Search Ranking

• Automatically Generating Labels Based on Unified Click Model

Libs

• GitHub - o19s/hello-ltr: Set of Jupyter notebooks demonstrating Learning to Rank integrated with Solr and Elasticsearch
• GitHub - varepsilon/clickmodels: ClickModels is a small set of Python scripts for the user click models initially developed at Yandex. A Click Model is a probabilistic graphical model used to predict search engine click data from past observations. This project is aimed to deal with click models used in Information Retrieval (see next README.md) and intended to be easy-to-read and easy-to-modify. If it's not, please let me know how to improve it :)
• GitHub - wikimedia/search-MjoLniR: Github mirror - our actual code is hosted with Gerrit (please see https://www.mediawiki.org/wiki/Developer_access for contributing)
• GitHub - markovi/PyClick
• GitHub - THUIR/PSCMModel

Videos

• PyData Tel Aviv Meetup: Learning To Rank - Uriel Vinetz - YouTube
• Applied Machine Learning for Ranking Products in an Ecommerce Setting Arnoud de Munnik Wehkamp Jerry - YouTube
• Conversion Models: Building Learning to Rank Training Data - Doug Turnbull, OpenSource Connections - YouTube
• Learning to Rank: From Theory to Production - Malvina Josephidou & Diego Ceccarelli, Bloomberg - YouTube
• Towards a Learning To Rank Ecosystem @ Snag - Xun Wang - YouTube

Books

• Click Models for Web Search, Morgan & Claypool Publishers

Mix

• https://arxiv.org/pdf/2102.07098.pdf
• http://www.dcs.gla.ac.uk/~craigm/publications/macdonald12inrt_ltr.pdf
• https://medium.com/adobetech/evaluating-addressing-position-bias-in-adobe-stock-search-9807b11ee268
• https://arxiv.org/pdf/1608.04468.pdf
• https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46485.pdf
• https://tech.ebayinc.com/engineering/using-behavioral-data-to-improve-search/
• https://tech.ebayinc.com/engineering/click-modeling-for-ecommerce/
• http://olivier.chapelle.cc/pub/DBN_www2009.pdf
• https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45286.pdf
• http://www.cs.cornell.edu/courses/cs6784/2014sp/lectures/18-AgichteinEtAl06.pdf
• https://www.youtube.com/watch?v=gvGfpc7dtMg
• https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46485.pdf
• http://www.xuanhui.me/pub/wsdm427-kang.pdf
• https://arxiv.org/pdf/1803.00710.pdf
• https://towardsdatascience.com/e-commerce-search-re-ranking-as-a-reinforcement-learning-problem-a9d1561edbd0
• https://tech.wayfair.com/data-science/2020/01/bayesian-product-ranking-at-wayfair/