BestHub
Sign in
Tag

query understanding

1 views collected around this technical thread.

DaTaobao Tech
DaTaobao Tech
Oct 18, 2023 · Artificial Intelligence

Large Model Application Challenges for E-commerce

Taobao Group’s ten large‑model e‑commerce challenges call for researchers to build domain‑specific data pipelines, mitigate forgetting, balance expertise with generality, enable multi‑step reasoning, handle long contexts, reduce hallucinations, integrate tool use, improve fuzzy intent detection, apply multi‑objective RLHF, and generate cognitively novel recommendations.

RLHFe-commerceknowledge hallucination
0 likes · 14 min read
Large Model Application Challenges for E-commerce
DataFunTalk
DataFunTalk
Dec 9, 2022 · Artificial Intelligence

POI Recognition and Alias Linking in Travel Search: Challenges, Algorithmic Practices, and Online Impact

The article presents a comprehensive study of POI (point‑of‑interest) recognition and alias linking within travel search, detailing background challenges, a multi‑stage algorithmic framework, extensive offline experiments, and the resulting improvements in online conversion and relevance.

Alias LinkingNLPPOI Recognition
0 likes · 14 min read
POI Recognition and Alias Linking in Travel Search: Challenges, Algorithmic Practices, and Online Impact
Dada Group Technology
Dada Group Technology
Oct 29, 2021 · Artificial Intelligence

Query Understanding in JD Daojia E‑commerce Search: Architecture, Core Algorithms, and Experimental Results

This article presents a comprehensive overview of JD Daojia's query understanding system for e‑commerce search, detailing its overall architecture, core modules such as tokenization, term weighting, query rewriting, intent detection, the algorithms employed, experimental evaluations, and future directions.

Natural Language ProcessingSearch Enginee-commerce
0 likes · 27 min read
Query Understanding in JD Daojia E‑commerce Search: Architecture, Core Algorithms, and Experimental Results
DataFunSummit
DataFunSummit
Jul 25, 2021 · Artificial Intelligence

Advances in Query Understanding and Semantic Retrieval at Zhihu Search

This article details Zhihu Search's engineering solutions for long‑tail query challenges, covering historical development, term weighting, synonym expansion, query rewriting with reinforcement learning, and semantic recall using BERT‑based models, while also outlining future research directions such as GAN‑based rewriting and lightweight pre‑training.

BERTembedding retrievalquery rewriting
0 likes · 14 min read
Advances in Query Understanding and Semantic Retrieval at Zhihu Search
DataFunTalk
DataFunTalk
Feb 3, 2021 · Artificial Intelligence

Travel Search Technology and Innovations at Alibaba Feizhu

This article presents an in‑depth overview of Alibaba Feizhu's travel‑scene search system, covering its background, architecture, query understanding, tagging, POI mining, synonym extraction, recall strategies, model designs, performance results, and future directions for personalization and explainability.

AINLPSearch
0 likes · 18 min read
Travel Search Technology and Innovations at Alibaba Feizhu
DataFunTalk
DataFunTalk
May 7, 2020 · Artificial Intelligence

Comprehensive Overview of Query Understanding in Search Engines

Query understanding (QU) involves lexical, syntactic, and semantic analysis of user queries to enable effective search recall and ranking, covering modules such as preprocessing, correction, expansion, segmentation, intent detection, term importance, and guidance, with detailed discussion of algorithms, models, and system architecture.

NLPSearch Engineinformation retrieval
0 likes · 51 min read
Comprehensive Overview of Query Understanding in Search Engines
DataFunTalk
DataFunTalk
Jan 2, 2020 · Artificial Intelligence

Improving Zhihu Search: Query Understanding, Term Weighting, Synonym Expansion, Query Rewriting, and Semantic Retrieval

This article details Zhihu's search engineering advances over the past year, covering long‑tail query challenges, term‑weight calculation, synonym expansion, query rewriting with translation models and reinforcement learning, and semantic retrieval using BERT‑based embeddings, while outlining future research directions.

NLPSearchquery rewriting
0 likes · 14 min read
Improving Zhihu Search: Query Understanding, Term Weighting, Synonym Expansion, Query Rewriting, and Semantic Retrieval
HomeTech
HomeTech
Nov 20, 2019 · Artificial Intelligence

Query Understanding and Intent Recognition in Search: Methods, Taxonomy, and Applications

This article explains how query understanding (QP) transforms user search queries into structured semantic blocks and intent categories using rule‑based NLP, entity recognition, and post‑processing, and describes its taxonomy, implementation details, and practical impact on search engine results.

NLPSearch Engineintent recognition
0 likes · 16 min read
Query Understanding and Intent Recognition in Search: Methods, Taxonomy, and Applications
DataFunTalk
DataFunTalk
Apr 19, 2019 · Artificial Intelligence

E-commerce Search and User Guidance: Concepts, Techniques, and Product Design

This article examines the role of search as a user guidance channel in e-commerce, outlining product requirements, user flow stages, and various algorithmic solutions—including query understanding, suggestion, rewriting, retrieval, and ranking—while also comparing implementations across major Chinese platforms.

RankingSearche-commerce
0 likes · 29 min read
E-commerce Search and User Guidance: Concepts, Techniques, and Product Design