re-ranking | BestHub

DataFunTalk

Aug 31, 2024 · Artificial Intelligence

Preference‑Oriented Diversity Model Based on Mutual Information for E‑commerce Search Re‑ranking (SIGIR 2024)

This paper, accepted at SIGIR 2024, introduces PODM‑MI, a preference‑oriented diversity re‑ranking model for e‑commerce search that jointly optimizes accuracy and diversity by modeling user intent with multivariate Gaussian distributions and maximizing mutual information between user preferences and candidate items.

diversitye-commerce searchmutual information

0 likes · 11 min read

Preference‑Oriented Diversity Model Based on Mutual Information for E‑commerce Search Re‑ranking (SIGIR 2024)

JD Tech

Aug 26, 2024 · Artificial Intelligence

Preference-oriented Diversity Model Based on Mutual Information for E-commerce Search Re-ranking (SIGIR 2024)

This article presents the SIGIR 2024 accepted PODM‑MI model, which uses variational inference and mutual‑information maximization to jointly optimize relevance and diversity in JD e‑commerce search re‑ranking, demonstrating significant gains in both user conversion and result diversity through extensive online experiments.

diversitye-commerce searchmutual information

0 likes · 11 min read

JD Tech Talk

Aug 26, 2024 · Artificial Intelligence

Preference-oriented Diversity Model Based on Mutual Information for E-commerce Re-ranking (SIGIR 2024)

The paper proposes PODM‑MI, a mutual‑information‑driven, preference‑oriented diversity model that jointly optimizes accuracy and diversity in e‑commerce search re‑ranking by modeling user preferences with multivariate Gaussian distributions and adapting rankings via a learnable utility matrix, showing significant gains in JD's main search experiments.

AIdiversitye-commerce search

0 likes · 12 min read

Preference-oriented Diversity Model Based on Mutual Information for E-commerce Re-ranking (SIGIR 2024)

JD Retail Technology

Aug 26, 2024 · Artificial Intelligence

Preference-oriented Diversity Model Based on Mutual Information for E-commerce Search Re-ranking (SIGIR 2024)

This article introduces PODM‑MI, a preference‑oriented diversity model that uses mutual information and variational Gaussian representations to jointly optimize accuracy and diversity in e‑commerce search re‑ranking, and reports significant online A/B test improvements on JD.com.

diversitye-commercemachine learning

0 likes · 10 min read

DataFunTalk

Aug 25, 2024 · Artificial Intelligence

Learning at Serving Time (LAST): An Online Learning Approach for Real‑Time Re‑ranking in Recommendation Systems

This article introduces LAST, a novel online learning method that updates ranking models instantly at serving time without waiting for user feedback, addressing the latency and stability challenges of real‑time re‑ranking in industrial recommendation pipelines and demonstrating its superiority through offline and online experiments.

Recommendation systemsmachine learningre-ranking

0 likes · 11 min read

Learning at Serving Time (LAST): An Online Learning Approach for Real‑Time Re‑ranking in Recommendation Systems

Sohu Tech Products

Dec 6, 2023 · Artificial Intelligence

Real-time Controllable Multi-Objective Re-ranking Models for Taobao Feed Recommendation

The paper introduces a real‑time controllable, multi‑objective re‑ranking framework for Taobao’s feed recommendation that combines actor‑critic reinforcement learning with hypernetworks to instantly adjust objective weights, handling diverse media and cold‑start constraints while delivering higher click‑through, diversity, and cold‑start ratios with only 20‑25 ms latency.

AlibabaReal-time ControlRecommendation systems

0 likes · 34 min read

Real-time Controllable Multi-Objective Re-ranking Models for Taobao Feed Recommendation

DataFunTalk

Nov 14, 2023 · Artificial Intelligence

Real-Time Controllable Multi-Objective Re‑ranking for Taobao Feed

This article presents a comprehensive study of a controllable multi‑objective re‑ranking model for Taobao's information‑flow recommendation, detailing the challenges of complex feed scenarios, three modeling paradigms (V1‑V3), an actor‑critic reinforcement learning framework with hypernet‑generated weights, and extensive online evaluation results.

Real-time ControlRecommendation systemshypernetworks

0 likes · 31 min read

Real-Time Controllable Multi-Objective Re‑ranking for Taobao Feed

JD Retail Technology

Aug 18, 2023 · Artificial Intelligence

Overview of Recommendation Systems: Definitions, Architecture, Recall, Ranking, and Re‑ranking

This article provides a comprehensive overview of recommendation systems, covering their definition, basic framework, request flow, AB testing, recall strategies (both non‑personalized and personalized), collaborative‑filtering methods, vector‑based retrieval, wide‑and‑deep models, and the MMR re‑ranking algorithm with code examples.

RankingRecommendation systemsVector Retrieval

0 likes · 14 min read

Overview of Recommendation Systems: Definitions, Architecture, Recall, Ranking, and Re‑ranking

Tencent Cloud Developer

Apr 7, 2022 · Artificial Intelligence

Re‑ranking in Recommendation Systems: Architecture, Techniques, and Efficiency

The article surveys the re‑ranking stage of modern recommendation pipelines, detailing its architecture after recall and precise ranking, and examining how shuffling and diversity improve user experience, while multi‑task fusion, context‑aware learning‑to‑rank, real‑time online learning, and traffic‑control strategies balance accuracy, efficiency, and business responsiveness.

Algorithmdiversitymachine learning

0 likes · 15 min read

Re‑ranking in Recommendation Systems: Architecture, Techniques, and Efficiency

DataFunTalk

Feb 10, 2022 · Artificial Intelligence

Evolution of Re‑ranking Techniques in Kuaishou Short‑Video Recommendation System

This article details the technical evolution of Kuaishou's short‑video recommendation pipeline, focusing on sequence re‑ranking, multi‑content mixing, and on‑device re‑ranking, and explains how transformer‑based models, generator‑evaluator frameworks, and reinforcement‑learning strategies are employed to maximize overall sequence value, user engagement, and revenue.

Kuaishoumulti-content mixingon-device inference

0 likes · 15 min read

Evolution of Re‑ranking Techniques in Kuaishou Short‑Video Recommendation System

DataFunSummit

Nov 19, 2021 · Artificial Intelligence

Sliding Spectrum Decomposition (SSD) for Diversified Recommendation in Re‑ranking

This article reviews the Sliding Spectrum Decomposition (SSD) model presented by Xiaohongshu at KDD 2021, explaining how it incorporates sliding‑window diversity into the re‑ranking stage, combines content‑based and collaborative‑filtering embeddings via the CB2CF framework, and demonstrates its effectiveness through offline and online A/B experiments.

diversityembeddingmachine learning

0 likes · 14 min read

Sliding Spectrum Decomposition (SSD) for Diversified Recommendation in Re‑ranking

DataFunTalk

Apr 17, 2021 · Artificial Intelligence

Personalized Re-ranking for Recommendation (ResSys'19)

This article introduces a personalized re‑ranking model for recommendation systems, explaining the limitations of traditional point‑wise ranking, describing the PRM architecture with input, encoding, and output layers using multi‑head attention and pre‑trained personalization features, and presenting experimental results and future extensions.

AttentionTransformerctr

0 likes · 7 min read

Personalized Re-ranking for Recommendation (ResSys'19)

DataFunTalk

Dec 17, 2020 · Artificial Intelligence

Context‑Aware Re‑ranking in Industrial Recommendation Systems: Design and Practice of a List Retrieval System

The article presents a comprehensive study of re‑ranking in large‑scale industrial recommendation pipelines, identifies four key challenges—context awareness, permutation specificity, computational complexity, and business constraints—and proposes a two‑stage List Retrieval System that combines fast sequence search and a generative re‑ranking network with a deep context‑wise model, achieving significant online gains across multiple Taobao feed scenarios.

Recommendation systemscontext-awaredeep learning

0 likes · 28 min read

Context‑Aware Re‑ranking in Industrial Recommendation Systems: Design and Practice of a List Retrieval System

iQIYI Technical Product Team

Jun 28, 2019 · Artificial Intelligence

Watchdog Team's TOP1 Solution for the iQIYI & ACMMM2019 Multimodal Video Person Recognition Challenge

Watchdog team won TOP1 in iQIYI & ACMMM2019 multimodal video person recognition challenge using pre‑extracted multimodal features, a 2048‑dim classifier with BCE loss, re‑ranking, DALI‑accelerated re‑detection, fine‑tuned InsightFace, and multi‑model ensembling achieving ~91% test accuracy.

deep learningfeature fusionmodel ensemble

0 likes · 12 min read

Watchdog Team's TOP1 Solution for the iQIYI & ACMMM2019 Multimodal Video Person Recognition Challenge