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Table of Contents
Introduction to Ranking
Conventional Ranking Methods
Query-Dependent Models
Query-Independent Models
Learning to Rank
Training/Test Sets
Goal
True Story
Basic Approaches
Pointwise Approach
Pairwise Approach
Listwise Approach
Query-Dependent Ranking
Options
References
Summary
Learning to Rank is a book that provides an introduction to ranking in the context of information retrieval. It covers various ranking methods, both query-dependent and query-independent. The book explores the concept of learning to rank, focusing on commercial attention and the use of training data in web search engines. Different approaches such as pointwise, pairwise, and listwise are discussed, along with the challenges and goals in optimizing for relevance degrees. The text also delves into the issue of query-dependent ranking and offers solutions for improving model efficiency and performance. References to relevant works in the field are provided, including the book 'Learning to Rank for Information Retrieval' by Tie-Yan Liu.