pairwise learning

Tencent Advertising Technology

Dec 20, 2022 · Artificial Intelligence

Modeling Advertising Attractiveness: Data Analysis, Pairwise Learning, and DeepFM Optimization

This article presents a comprehensive study on estimating video ad attractiveness by analyzing 3‑second completion rates, proposing pairwise MLP and DeepFM models, introducing hierarchical sampling and multimodal features, and demonstrating practical deployment improvements in material recommendation and ad ranking.

advertisingattractivenessdeepFM

0 likes · 16 min read

Modeling Advertising Attractiveness: Data Analysis, Pairwise Learning, and DeepFM Optimization

Tencent Advertising Technology

Dec 15, 2022 · Artificial Intelligence

AI‑Driven Element Selection for Advertising Video Creative Generation

This article explains how Tencent's advertising system leverages multimedia AI techniques—including multi‑armed bandit, pairwise learning, and DeepFM models—to automatically select optimal templates, music, and stickers for image and video assets, thereby reducing production cost, improving creative quality, and boosting ad performance.

MABadvertising AIdeepFM

0 likes · 17 min read

AI‑Driven Element Selection for Advertising Video Creative Generation

Model Perspective

Oct 13, 2022 · Artificial Intelligence

How BPR Transforms Recommendation Ranking: A Deep Dive

The article introduces the Bayesian Personalized Ranking (BPR) algorithm, explains its background in ranking‑based recommendation, details its probabilistic modeling assumptions, optimization objective, gradient‑based learning process, and compares it with matrix‑factorization methods like FunkSVD, providing a concise training workflow.

BPRRankingRecommendation systems

0 likes · 8 min read

How BPR Transforms Recommendation Ranking: A Deep Dive

DataFunTalk

Feb 14, 2022 · Artificial Intelligence

Optimizing QQ Music Ranking Models: From Pairwise Methods to Multi‑Objective Learning and Causal Inference

This talk details the evolution of QQ Music's ranking system, covering background, user‑perception modeling, pairwise optimization, advanced model architectures, multi‑objective learning with causal inference to mitigate the Matthew effect, cross‑domain recommendation, and module personalization that together boost user engagement and platform traffic.

causal inferencecross-domain recommendationmulti-objective learning

0 likes · 16 min read

Optimizing QQ Music Ranking Models: From Pairwise Methods to Multi‑Objective Learning and Causal Inference

NetEase Media Technology Team

Apr 26, 2019 · Artificial Intelligence

Intelligent Cover Image Selection System for News Articles: Image Quality Assessment and Smart Cropping

The article describes an intelligent cover‑image selection system for NetEase News that automatically filters unsuitable illustrations, assesses image quality with a pairwise‑trained deep model across clarity, color and composition, and smartly crops images using aspect‑ratio‑aware object detection, dramatically cutting manual editing and enabling confidence‑based automatic publishing.

Image Croppingcomputer visioncontent-aware image resizing

0 likes · 11 min read

Intelligent Cover Image Selection System for News Articles: Image Quality Assessment and Smart Cropping

DataFunTalk

Apr 25, 2019 · Artificial Intelligence

Comparison of Classification and Ranking Models in Recommendation Systems

This article examines the differences and similarities between classification (pointwise) and ranking (pairwise) models for recommendation systems, covering their probabilistic foundations, loss functions, parameter updates, and practical implications such as sensitivity to statistical features and robustness.

Recommendation systemsclassification modelloss function

0 likes · 10 min read

Comparison of Classification and Ranking Models in Recommendation Systems

Tencent Cloud Developer

Mar 16, 2018 · Artificial Intelligence

Pairwise Ranking Factorization Machines (PRFM) for Feed Recommendation in Tencent Shield

The article presents Pairwise Ranking Factorization Machines (PRFM), a pairwise‑learning extension of Factorization Machines that replaces Tencent Shield’s pointwise binary‑classification pipeline, generates user‑item‑item triples, optimizes a cross‑entropy loss, and achieves about a 5% relative UV click‑through gain on the HandQ anime feed while outlining offline metrics, hyper‑parameter tuning, and future informed‑sampling enhancements.

Factorization MachinesRankingRecommendation systems

0 likes · 10 min read