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

DataFunTalk

May 25, 2020 · Artificial Intelligence

NLP Techniques for Financial Investment Analysis: Case Studies from Two Sigma, BlackRock, UC Berkeley and Others

This article reviews how natural language processing is used in financial investment analysis, summarizing case studies from Two Sigma, BlackRock, UC Berkeley and other institutions that apply topic modeling, event extraction and sentiment analysis to improve portfolio performance and achieve excess returns.

Event ExtractionInvestment AnalysisNLP

0 likes · 17 min read

NLP Techniques for Financial Investment Analysis: Case Studies from Two Sigma, BlackRock, UC Berkeley and Others

Meitu Technology

Jul 17, 2018 · Artificial Intelligence

Video Clustering Techniques for Personalized Recommendation in Meipai

Meipai’s personalized recommendation system leverages massive user‑behavior data to build behavior‑driven video clusters—evolving from TopicModel through Item2vec and Keyword Propagation to a DSSM deep model—boosting ranking AUC, enhancing UI diversity, similar‑video search, niche discovery, and feature engineering.

DSSMItem2Veckeyword propagation

0 likes · 22 min read

Video Clustering Techniques for Personalized Recommendation in Meipai

Hulu Beijing

Jan 11, 2018 · Artificial Intelligence

Topic Modeling Explained: pLSA, LDA, and How to Pick the Right Number of Topics

This article introduces the fundamentals of topic modeling, compares the probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA) methods, explains their graphical models and inference via EM or Gibbs sampling, and discusses practical strategies for selecting the optimal number of topics using perplexity or hierarchical Dirichlet processes.

LDAPerplexitypLSA

0 likes · 10 min read

Topic Modeling Explained: pLSA, LDA, and How to Pick the Right Number of Topics

21CTO

Sep 1, 2015 · Artificial Intelligence

How the NYT Revamped Its Recommendation Engine with Collaborative Topic Modeling

This article explains how the New York Times redesigned its "Recommended for You" system by combining content‑based filtering, collaborative filtering, and a collaborative topic‑modeling approach that uses LDA, reader‑signal adjustments, and fast preference calculations to deliver personalized article suggestions.

LDARecommendation Systemscollaborative filtering

0 likes · 12 min read

How the NYT Revamped Its Recommendation Engine with Collaborative Topic Modeling