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DataFunSummit

Mar 24, 2022 · Artificial Intelligence

An Overview of Learning to Rank (LTR) Models: Point‑wise, Pair‑wise, List‑wise, and Generative Approaches

This article provides a comprehensive introduction to Learning to Rank (LTR), describing its four major categories—point‑wise, pair‑wise, list‑wise, and generative models—along with typical algorithms such as Wide & Deep, ESMM, RankNet, LambdaRank, LambdaMART, DLCM, and miRNN, and discusses their architectures, loss functions, and practical considerations in advertising and recommendation systems.

Generative ModelsMachine LearningPairwise

0 likes · 22 min read

An Overview of Learning to Rank (LTR) Models: Point‑wise, Pair‑wise, List‑wise, and Generative Approaches

DataFunSummit

Aug 8, 2021 · Artificial Intelligence

Diversity as a Means, Not an End, in Recommendation Systems

The article argues that diversity in recommendation systems should be treated as a means rather than an ultimate goal, explains why it is hard to quantify, suggests using real performance metrics such as click‑through rate and dwell time, and offers practical strategies to improve listwise ranking.

Machine LearningRankingdiversity

0 likes · 7 min read

Diversity as a Means, Not an End, in Recommendation Systems

DataFunTalk

Sep 20, 2019 · Artificial Intelligence

Diversity as a Means, Not an End, in Recommendation Systems

The article argues that diversity should be treated as a tool rather than a final objective in recommendation systems, explains why it is hard to quantify, discusses appropriate metrics such as user feedback and engagement, and presents practical strategies—including expert rules, richer recall pipelines, and list‑wise modeling—to improve diversity while optimizing true business goals.

Machine LearningRankingdiversity

0 likes · 7 min read

vivo Internet Technology

Jan 22, 2018 · Artificial Intelligence

Learning to Rank: From Regression to Search Ranking and Evaluation Methods

Learning to rank reframes search as a machine‑learning problem that optimizes document ordering rather than numeric prediction, using relevance metrics such as NDCG and feature‑based scoring functions, and comparing point‑wise, pair‑wise (RankSVM) and list‑wise (ListNet) approaches while stressing that proper error definition and feature selection matter more than the specific algorithm.

Machine LearningNDCGPairwise

0 likes · 16 min read

Learning to Rank: From Regression to Search Ranking and Evaluation Methods

Qunar Tech Salon

Jan 13, 2016 · Artificial Intelligence

Ranking Learning in Mobile Taobao: Challenges, Solutions, and Improvements

This article presents a comprehensive overview of ranking learning techniques used in Mobile Taobao's recommendation system, covering problem definition, pointwise/pairwise/listwise approaches, feature engineering, online learning, industry applications, and future optimization strategies.

CTR predictionLambdaMARTMachine Learning

0 likes · 8 min read

Ranking Learning in Mobile Taobao: Challenges, Solutions, and Improvements