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Table of Contents
Learning to Rank: From Heuristics to Theoretic Approaches
Relevance Estimation
Machine Learning
Subset Ranking using Regression
Ranking with Large Margin Principles
Pointwise Learning to Rank
Ranking with Large Margin Principles
Pairwise Learning to Rank
Optimizing Search Engines using Clickthrough Data
A Regression Framework for Learning Ranking Functions Using Relative Relevance Judgments
Pairwise Learning to Rank
Optimizing Search Engines using Clickthrough Data
An Efficient Boosting Algorithm for Combining Preferences
A Regression Framework for Learning Ranking Functions Using Relative Relevance Judgments
Optimizing Search Engines using Clickthrough Data
From RankNet to LambdaRank to LambdaMART: An Overview
From RankNet to LambdaRank to LambdaMART: An Overview
A Lambda tree
A Support Vector Machine for Optimizing Average Precision
Other listwise solutions
Summary
Experimental Comparisons
Connection with Traditional IR
Analysis of the Approaches
Broader Notion of Relevance
Future
Resources
References
Summary
The document explores the topic of Learning to Rank, delving into various approaches and techniques. It covers concepts such as Subset Ranking using Regression, Ranking with Large Margin Principles, Pairwise Learning to Rank, Listwise Learning to Rank, and more. The document discusses optimizing search engines using clickthrough data, boosting algorithms, regression frameworks, support vector machines, and other listwise solutions. Additionally, it provides experimental comparisons of different methods and their connections with traditional Information Retrieval. The text also highlights the broader notion of relevance in the context of ranking. The document concludes with insights into future directions, available resources, and a list of references for further reading.