search ranking | BestHub

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

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

Baidu Tech Salon

Dec 14, 2023 · Artificial Intelligence

Baidu Research Institute 2023 Paper Sharing Session – Presented Papers Overview

The Baidu Research Institute’s 2023 Paper Sharing Session featured eight cutting‑edge papers—from semi‑supervised web‑search ranking and hierarchical reinforcement learning for autonomous intersections to spatial‑heterophily graph networks, a unified XAI benchmark, differentiable neuro‑symbolic KG reasoning, and novel stochastic‑gradient and neural‑field loss analyses—showcasing advances across AI, data mining, and computer vision.

Artificial IntelligenceGraph Neural NetworksKnowledge Graphs

0 likes · 10 min read

Baidu Research Institute 2023 Paper Sharing Session – Presented Papers Overview

Airbnb Technology Team

Aug 3, 2023 · Artificial Intelligence

Improving Airbnb Search Ranking Diversity with Neural Networks

Airbnb upgraded its neural‑network ranking system by adding a similarity network that penalizes duplicate‑like listings, enabling the algorithm to present a more diverse set of options, which boosted booking rates, value, and five‑star ratings, demonstrating that reduced result similarity improves overall search quality.

Airbnbdiversitymachine learning

0 likes · 8 min read

Improving Airbnb Search Ranking Diversity with Neural Networks

DataFunSummit

Jul 14, 2023 · Artificial Intelligence

Iterative Evolution of JD Search EE System: Adaptive Exploration, Scenario Modeling, Scoring‑Insertion Consistency, and Context‑Aware Brand Store Detection

This article details the multi‑stage evolution of JD's search Explore‑Exploit (EE) system—covering an adaptive dynamic detection model, scenario‑modeling upgrades, end‑to‑end scoring and insertion consistency, and context‑aware brand/store dimension detection—demonstrating how each iteration improves result diversity, user experience, and key online metrics while maintaining search efficiency.

adaptive modelinge-commerceexplore‑exploit

0 likes · 24 min read

DataFunTalk

Jan 28, 2023 · Artificial Intelligence

Industry Search: Background, Technologies, and Real‑World Applications

This article presents a comprehensive overview of industry search, covering its background, core retrieval and ranking technologies—including sparse and dense retrieval, pre‑trained language models, tokenization, NER, adaptive multi‑task training, and re‑ranking models—followed by detailed case studies such as address analysis, family‑ID unification, emergency call handling, education photo‑search, and power‑knowledge‑base integration.

NLPaddress analysisindustry search

0 likes · 13 min read

Industry Search: Background, Technologies, and Real‑World Applications

DaTaobao Tech

Jan 6, 2023 · Artificial Intelligence

Two‑Stage Ranking Optimization in E‑commerce Search: From Coarse to Fine Ranking

The paper presents a two‑stage e‑commerce search framework where the coarse‑ranking stage is redesigned with multi‑objective optimization, expanded negative sampling, and listwise distillation—guided by a new global transaction hitrate metric—enabling it to surpass fine‑ranking on large candidate sets and boost overall GMV by about one percent.

coarse rankinge-commercefine ranking

0 likes · 25 min read

Two‑Stage Ranking Optimization in E‑commerce Search: From Coarse to Fine Ranking

DataFunSummit

Aug 14, 2022 · Artificial Intelligence

Optimizing Pre‑Ranking in Meituan Search: Knowledge Distillation and Neural Architecture Search

This article describes Meituan Search's pre‑ranking (coarse‑ranking) system evolution and presents two major optimization strategies—leveraging knowledge distillation to align coarse‑ranking with fine‑ranking and employing neural architecture search to jointly improve effectiveness and latency—demonstrating significant offline and online performance gains.

Neural Architecture Searchknowledge distillationmachine learning

0 likes · 17 min read

Optimizing Pre‑Ranking in Meituan Search: Knowledge Distillation and Neural Architecture Search

DataFunTalk

Jun 24, 2022 · Artificial Intelligence

Explore‑and‑Exploit (EE) in JD Search: Bias Mitigation, Model Iteration, and Evaluation

The talk presents JD Search's Explore‑and‑Exploit (EE) module, detailing its bias‑mitigation pipeline—including position, popularity, and exposure debiasing—model architecture upgrades with SVGP and causal inference, online AB metrics, offline evaluation methods, and future research directions to improve search diversity and long‑term value.

Bias MitigationSVGPcausal inference

0 likes · 17 min read

Explore‑and‑Exploit (EE) in JD Search: Bias Mitigation, Model Iteration, and Evaluation

DataFunSummit

May 16, 2022 · Artificial Intelligence

Reinforcement Learning for E‑commerce Search Ranking: RNN User State Modeling and DDPG Long‑Term Value Optimization

This presentation details how JD applied reinforcement learning—using RNN‑based user state modeling and a DDPG framework—to improve e‑commerce search ranking by optimizing long‑term cumulative value, showing significant offline and online gains in conversion and GMV.

DDPGRNNe-commerce

0 likes · 20 min read

Reinforcement Learning for E‑commerce Search Ranking: RNN User State Modeling and DDPG Long‑Term Value Optimization

DataFunSummit

May 10, 2022 · Artificial Intelligence

Optimizing Fliggy Search Ranking with Product Inclusion Relationships: The DIRN Model

This article presents the DIRN model, which leverages product inclusion graphs and graph‑based embeddings to address the challenges of ranking both single‑item and complex travel products on Fliggy, demonstrating significant CTR, CVR, and GMV improvements through offline experiments and online A/B testing.

AlibabaCTR predictionDIRN

0 likes · 13 min read

Optimizing Fliggy Search Ranking with Product Inclusion Relationships: The DIRN Model

DataFunTalk

Jan 24, 2022 · Artificial Intelligence

Meituan Search Ranking: Multi‑Business Sorting Architecture and Optimization Practices

This article presents Meituan's search ranking system, detailing its multi‑business sorting architecture, layered ranking pipeline, quota and fine‑ranking models, aggregation modeling techniques, and supporting platforms such as Lego and Poker, while also sharing practical insights and recruitment information.

AIMeituanRecommendation systems

0 likes · 16 min read

Meituan Search Ranking: Multi‑Business Sorting Architecture and Optimization Practices

DataFunTalk

Aug 7, 2021 · Artificial Intelligence

Multi-Category Mixture-of-Experts Model for JD Search Ranking

This article presents a multi‑category Mixture‑of‑Experts (MoE) approach for e‑commerce search ranking, addressing category‑specific behavior and small‑category learning by introducing hierarchical soft constraints and adversarial regularization, and demonstrates significant AUC and NDCG gains on Amazon and JD in‑house datasets.

Adversarial RegularizationHierarchical Soft ConstraintMixture of Experts

0 likes · 10 min read

Multi-Category Mixture-of-Experts Model for JD Search Ranking

iQIYI Technical Product Team

Jul 30, 2021 · Artificial Intelligence

iQIYI Search Ranking Algorithm Practice – NLP and Search Integration

At iQIYI’s iTech Conference, Zhang Zhigang detailed a full‑stack search ranking system that combines NLP‑driven query analysis, hierarchical indexing, multi‑stage coarse‑to‑fine ranking, Transformer‑based re‑ranking, sparse‑feature DNN enhancements and LIME/SE‑Block explainability, delivering measurable gains in CTR and NDCG for the platform’s video search.

NLPiQIYIinformation retrieval

0 likes · 20 min read

iQIYI Search Ranking Algorithm Practice – NLP and Search Integration

DataFunTalk

Jul 10, 2021 · Artificial Intelligence

Multi‑Business Ranking Modeling and Optimization in Meituan Search

This article presents Meituan's multi‑business search ranking system, describing the challenges of mixed‑business queries, the layered architecture, the evolution of multi‑business quota models (MQM‑V1/V2) and multi‑business ranking networks (MBN‑V1‑V4), experimental results, and future research directions.

Meituandeep learninge-commerce

0 likes · 16 min read

Multi‑Business Ranking Modeling and Optimization in Meituan Search

DataFunTalk

May 31, 2021 · Artificial Intelligence

Intelligent Transportation Search Ranking: From Business Rules to Personalized Ranking Models

This article presents the challenges of travel‑related product search, explains why traditional rule‑based sorting is insufficient, and describes how Alibaba Flypig’s team built a deep‑learning based personalized ranking system—including architecture, model variants, experimental results, and future optimization directions—to improve conversion rates for flight and ticket searches.

AIdeep learningpersonalized recommendation

0 likes · 9 min read

Intelligent Transportation Search Ranking: From Business Rules to Personalized Ranking Models

DataFunTalk

Mar 1, 2021 · Artificial Intelligence

Online Learning and Real‑Time Model Updating in JD Retail Search Using Flink

The article describes JD's end‑to‑end online learning pipeline for retail search, covering the background, system architecture, real‑time feature collection, sample stitching, Flink‑based incremental training, parameter updates, and full‑link monitoring to achieve low‑latency, high‑accuracy model serving.

Feature EngineeringFlinkmodel serving

0 likes · 9 min read

Online Learning and Real‑Time Model Updating in JD Retail Search Using Flink

DataFunTalk

Feb 10, 2021 · Artificial Intelligence

Deep Learning Based Search Ranking Optimization for 58.com Rental Services

This article describes how 58.com’s rental platform leverages deep learning models such as Wide&Deep, DeepFM, DCN, DIN, and DIEN to improve search ranking, detailing data pipelines, feature engineering, model iteration, multi‑task training, prediction optimizations, and resulting online performance gains.

Feature EngineeringRecommendation systemsdeep learning

0 likes · 27 min read

Deep Learning Based Search Ranking Optimization for 58.com Rental Services

DataFunTalk

Jan 25, 2021 · Artificial Intelligence

Evolution of Zhihu Search Ranking Models: From GBDT to DNN, Multi‑Goal and Context‑Aware LTR

This article reviews the development of Zhihu's search system, describing the transition from early GBDT ranking to deep neural networks, the introduction of multi‑objective and position‑bias‑aware learning‑to‑rank methods, context‑aware techniques, end‑to‑end training, personalization, and future research directions.

DNNGBDTdeep learning

0 likes · 17 min read

Evolution of Zhihu Search Ranking Models: From GBDT to DNN, Multi‑Goal and Context‑Aware LTR

58 Tech

Jan 25, 2021 · Artificial Intelligence

Deep Learning Ranking Models for 58.com Rental Search: Architecture, Model Iterations, and Optimization

This article presents the end‑to‑end design, feature engineering, model evolution (Wide&Deep, DeepFM, DCN, DIN, DIEN), multi‑task training, and deployment optimizations that 58.com applied to improve search ranking for its rental business, demonstrating significant gains in click‑through and conversion rates.

Feature Engineeringdeep learningmodel optimization

0 likes · 28 min read

Deep Learning Ranking Models for 58.com Rental Search: Architecture, Model Iterations, and Optimization

System Architect Go

Nov 2, 2020 · Backend Development

Custom Scoring in Elasticsearch Using function_score

Elasticsearch calculates a relevance score for each document, but using the function_score query you can customize this scoring by combining the original query_score with a user-defined func_score through various functions such as weight, random_score, field_value_factor, decay_function, and script_score, allowing flexible ranking based on business needs.

Elasticsearchbackendcustom scoring

0 likes · 11 min read

Custom Scoring in Elasticsearch Using function_score