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Dec 24, 2025 · Artificial Intelligence

Building an ASR+LLM+Vector Knowledge Base for Precise Video Ad Category Detection

This article presents a layered ASR‑LLM‑vector‑knowledge‑base pipeline that cleans speech transcripts, semantically repairs text, performs hierarchical exact and fuzzy matching, and iteratively refines mappings to accurately identify product categories in video advertisements, while detailing module functions, technical choices, and LLM parameter tuning.

ASRKnowledge BaseLLM

0 likes · 11 min read

Building an ASR+LLM+Vector Knowledge Base for Precise Video Ad Category Detection

Bighead's Algorithm Notes

Nov 24, 2025 · Industry Insights

STRAPSim: A Component‑Level Portfolio Similarity Metric for ETF Alignment and Trade Execution

The paper introduces STRAPSim, a semantic, two‑stage, residual‑aware similarity measure that captures component‑level semantics and weight distribution for ETFs, and demonstrates through extensive toy and corporate‑bond ETF experiments that it consistently outperforms Jaccard, weighted Jaccard and BERTScore variants in classification, regression, recommendation and Spearman correlation tasks.

ETF similarityFinancial AISTRAPSim

0 likes · 13 min read

STRAPSim: A Component‑Level Portfolio Similarity Metric for ETF Alignment and Trade Execution

Xianyu Technology

Feb 22, 2023 · Artificial Intelligence

Integrating Retrieval and Generation Tasks for Deep Semantic Matching in Xianyu Search

The paper introduces SimBert, a later‑fusion model that jointly trains a dual‑tower retrieval component and an auxiliary generation task on the item tower, using a two‑stage pre‑training and fine‑tuning pipeline, which yields a 3.6% relevance boost and reduces bad‑case rates in Xianyu search.

BERTmulti-task trainingretrieval-generation

0 likes · 8 min read

Integrating Retrieval and Generation Tasks for Deep Semantic Matching in Xianyu Search

Youzan Coder

Oct 24, 2022 · Artificial Intelligence

Knowledge Base Retrieval Matching: Algorithm and Engineering Service Practice

The article outlines a comprehensive knowledge‑base retrieval matching solution—combining PageRank‑enhanced DSL rewriting, keyword and dual‑tower vector recall, contrastive fine‑ranking, and optimized vector‑based ranking—implemented via offline DP training and Sunfish online inference on Milvus, with applications in enterprise search and recommendations and future plans for graph‑neural embeddings.

InfoNCEMilvusNLP

0 likes · 12 min read

Knowledge Base Retrieval Matching: Algorithm and Engineering Service Practice

Ctrip Technology

Jun 16, 2022 · Artificial Intelligence

Entity Linking System for Travel Knowledge Graph at Ctrip AI R&D

The article presents Ctrip's travel AI team's end‑to‑end entity linking solution built on a large‑scale tourism knowledge graph, detailing its background, technical architecture, core modules—including mention detection, candidate generation, and disambiguation using BERT and prefix‑tree techniques—and real‑world applications such as search, intelligent客服, and POI data maintenance.

BERTKnowledge GraphNLP

0 likes · 18 min read

Entity Linking System for Travel Knowledge Graph at Ctrip AI R&D

DataFunSummit

Feb 12, 2022 · Artificial Intelligence

Advances and Challenges in Post‑BERT Semantic Matching: Negative Sampling, Data Augmentation, and Applications

After the BERT era, this article reviews the limitations of pre‑trained language models for semantic matching, discusses negative‑sample sampling, data‑augmentation techniques, contrastive learning methods such as ConSERT and SimCSE, and practical deployment considerations in vector‑based retrieval systems.

contrastive learningdata augmentationpretrained language models

0 likes · 20 min read

Advances and Challenges in Post‑BERT Semantic Matching: Negative Sampling, Data Augmentation, and Applications

Ctrip Technology

Dec 30, 2021 · Artificial Intelligence

Semantic Matching Techniques for Intelligent Customer Service at Ctrip

This article presents Ctrip's intelligent customer service system, detailing the evolution of semantic matching methods from traditional lexical models to deep learning approaches such as BERT and ESIM, and describing multi‑stage retrieval, multilingual transfer learning, and KBQA techniques for improving query understanding and response accuracy.

BERTNLPcustomer-service

0 likes · 16 min read

Semantic Matching Techniques for Intelligent Customer Service at Ctrip

DataFunTalk

Oct 13, 2021 · Artificial Intelligence

Intelligent Recruitment: Deep Semantic Matching, Interview Assistance, and Text Representation

This article explores how AI techniques such as deep semantic matching, attention mechanisms, variational autoencoders, and neural topic models can transform traditional recruitment by improving person‑job matching, interview assistance, and text representation, supported by experiments on real‑world hiring data.

AI RecruitmentVAEinterview assistance

0 likes · 18 min read

Intelligent Recruitment: Deep Semantic Matching, Interview Assistance, and Text Representation

DataFunSummit

Oct 13, 2021 · Artificial Intelligence

Intelligent Recruitment: Deep Semantic Matching, Interview Assistance, and Text Representation with VAE and Neural Topic Models

This article presents a comprehensive overview of applying AI techniques—semantic matching models, attention mechanisms, VAE‑based text representation, and neural topic models—to improve talent acquisition, candidate‑job matching, interview assistance, and recruitment text analysis, supported by experiments on real‑world hiring data.

AI in HRIntelligent RecruitmentNeural Topic Model

0 likes · 19 min read

Intelligent Recruitment: Deep Semantic Matching, Interview Assistance, and Text Representation with VAE and Neural Topic Models

DataFunTalk

Nov 16, 2020 · Artificial Intelligence

Deep Semantic Relevance and Multimodal Video Search at Alibaba Entertainment

The presentation by Alibaba Entertainment's senior algorithm expert details the challenges of video search in the 4G/5G era and describes a comprehensive framework covering business overview, relevance and ranking, multimodal retrieval, deep semantic modeling, dataset construction, and practical deployment techniques.

Deep Learninginformation retrievalmultimodal

0 likes · 27 min read

Deep Semantic Relevance and Multimodal Video Search at Alibaba Entertainment

Ctrip Technology

Jun 4, 2020 · Artificial Intelligence

Semantic Matching Models for Travel QA: Deep Learning Techniques, Interaction Models, and Transfer Learning

This article reviews the evolution of semantic matching models for travel question‑answering, covering traditional keyword and probabilistic methods, deep‑learning encoders such as LSTM, CNN, and Transformer, interaction‑based architectures like MatchPyramid and hCNN, as well as transfer‑learning and multilingual extensions to improve practical deployment.

Deep Learningcontext modelingnatural language processing

0 likes · 21 min read

Semantic Matching Models for Travel QA: Deep Learning Techniques, Interaction Models, and Transfer Learning

Ctrip Technology

Apr 30, 2020 · Artificial Intelligence

Intelligent Generation of Search Engine Advertising Keywords: Methods, Frameworks, and Future Directions

This article presents a comprehensive overview of automated techniques for generating high‑quality search engine advertising keywords, covering background, traditional manual methods, intelligent keyword expansion using NLP, segmentation, POS tagging, BILSTM‑CRF, BERT classification, semantic matching with DSSM, and additional approaches such as query suggestion and synonym rewriting.

BERTBILSTM-CRFNLP

0 likes · 15 min read

Intelligent Generation of Search Engine Advertising Keywords: Methods, Frameworks, and Future Directions

Ctrip Technology

Jan 16, 2020 · Artificial Intelligence

Ctrip's Marco Polo Platform: AI‑Driven Content Generation, Semantic Matching, and Productization

The article details Ctrip’s Marco Polo content platform, describing its data, algorithm, and functional layers, and explains how AI techniques such as NLP, semantic matching, named‑entity recognition, and image classification are applied to automate product‑centric content mining, article generation, quality rating, and first‑image selection.

AICtripImage Classification

0 likes · 16 min read

Ctrip's Marco Polo Platform: AI‑Driven Content Generation, Semantic Matching, and Productization

DataFunTalk

Dec 21, 2018 · Artificial Intelligence

Iterative Evolution of iQIYI Video Search Ranking Models

This article details iQIYI's practical experience in building and iterating its video search system, covering basic relevance, semantic matching via translation and click models, deep‑learning approaches, and ranking model evolution from heuristic rules to learning‑to‑rank, highlighting challenges, solutions, and performance gains.

machine learningsearch rankingsemantic matching

0 likes · 20 min read

Iterative Evolution of iQIYI Video Search Ranking Models

Meituan Technology Team

Feb 10, 2017 · Artificial Intelligence

Deep Learning Applications in Semantic Matching, Image Quality Ranking, and OCR at Meituan-Dianping

Meituan‑Dianping leverages deep‑learning models—including ClickNet for semantic search matching, an AlexNet‑based image‑quality ranker, and a Faster‑RCNN/FCN‑driven OCR pipeline—to personalize results, select attractive POI images, and extract text, achieving higher click‑through rates, conversions, and operational efficiency across its O2O services.

AI applicationsMeituanOCR

0 likes · 13 min read

Deep Learning Applications in Semantic Matching, Image Quality Ranking, and OCR at Meituan-Dianping

Alibaba Cloud Developer

Sep 28, 2016 · Artificial Intelligence

How Deep Learning is Revolutionizing Automatic Question Answering

This article reviews the evolution of automatic question answering systems, outlines their core processing framework, and details how deep neural networks—especially CNNs, RNNs, and DCNNs—enable semantic representation, matching, and answer generation, while also discussing current challenges and future directions.

Deep LearningNeural Networksnatural language processing

0 likes · 27 min read

How Deep Learning is Revolutionizing Automatic Question Answering

Ctrip Technology

Aug 5, 2016 · Artificial Intelligence

Advances in Deep Learning for Speech and Semantic Understanding: Insights from Huawei Noah's Ark Lab

The article reviews a decade of deep‑learning breakthroughs, highlights Huawei Noah's recent research on speech, image and natural‑language processing, and discusses future trends such as neural‑symbolic integration, end‑to‑end learning, and knowledge‑driven AI systems.

AI researchHuaweinatural language processing

0 likes · 8 min read

Advances in Deep Learning for Speech and Semantic Understanding: Insights from Huawei Noah's Ark Lab