New Precise Matching Techniques from JD’s SIGIR 2025 Papers

JD's retail technology team presents five SIGIR 2025 papers that introduce advanced graph neural, causal optimal transport, domain‑oriented relevance, multi‑objective bid‑word generation, and hierarchical user behavior models to dramatically improve precise matching in e‑commerce search, recommendation, and advertising.

AdvertisingCTR predictioncausal optimal transport

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New Precise Matching Techniques from JD’s SIGIR 2025 Papers

Alimama Tech

Apr 23, 2025 · Artificial Intelligence

Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning

The paper introduces an explainable LLM framework (ELLM‑rele) that uses chain‑of‑thought reasoning and a multi‑dimensional knowledge distillation pipeline to compress large‑model relevance judgments into lightweight student models, achieving superior offline relevance scores and online click‑through and conversion improvements in Taobao’s search advertising.

LLMchain-of-thoughtexplainability

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Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning

DataFunTalk

Aug 13, 2023 · Artificial Intelligence

Applying Large Language Models to Search Advertising Satisfaction: From DNN to ERNIE and Prompt Learning

The article details how Baidu's Fengchao team leverages large language models, including a transition from DNN embeddings to ERNIE, introduces multi‑level tokenization and discrete core‑word inputs, and applies prompt learning and AIGC techniques to improve search advertising satisfaction and industry‑specific relevance modeling.

AIGCBaidularge language models

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Applying Large Language Models to Search Advertising Satisfaction: From DNN to ERNIE and Prompt Learning

NetEase Cloud Music Tech Team

Jan 4, 2023 · Artificial Intelligence

Relevance Modeling and Ranking for Cloud Music Video Search

The paper details Cloud Music’s video‑search pipeline—query understanding, recall, relevance, ranking and re‑ranking—highlighting challenges such as ambiguous content, timeliness and multi‑objective goals, and describes two deployed models (a twin‑tower aspect relevance network and a click‑graph propagator) that together boost click‑through rate by 1.5 % and effective CTR by 2.3 %.

click graphmultimodalranking

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Relevance Modeling and Ranking for Cloud Music Video Search

DataFunTalk

Nov 16, 2021 · Artificial Intelligence

Hotel Search Relevance Modeling and Architecture at Fliggy (Alibaba)

This article presents a comprehensive overview of Fliggy's hotel search relevance system, covering the business background, multi‑scenario architecture, core factor estimation, entity recognition, text and spatial relevance modeling, multi‑scenario fusion, and future optimization directions.

AIBERThotel search

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Hotel Search Relevance Modeling and Architecture at Fliggy (Alibaba)

DataFunSummit

Nov 15, 2021 · Artificial Intelligence

Hotel Search Relevance Construction and Modeling at Fliggy (Alibaba)

This article presents a comprehensive overview of Fliggy's hotel search system, covering its multi‑platform background, architecture, complex relevance factors—including text, spatial, and price—and the modeling techniques used to fuse these signals for personalized ranking, along with future improvement directions.

AIhotel searchpersonalization

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Hotel Search Relevance Construction and Modeling at Fliggy (Alibaba)

DataFunTalk

Dec 14, 2020 · Artificial Intelligence

Query Expansion Techniques: Relevance Modeling vs. Generative Approaches and Future Directions

This article reviews current query expansion methods, contrasting relevance‑based models that rely on terms or entities with generative models that encode whole queries, discusses challenges of handling long and complex queries, and surveys recent research on encoding queries, session modeling, and multi‑task feature integration.

Generative ModelsNLPinformation retrieval

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Query Expansion Techniques: Relevance Modeling vs. Generative Approaches and Future Directions