Transformer‑Based Denoising AutoEncoder (TSDAE) for Job Description Embeddings (Job2Vec)

This article explains how TSDAE, a transformer‑based denoising auto‑encoder, converts noisy job description sentences into robust vector embeddings, details its training process, loss function, dataset preparation, and demonstrates using FAISS for similarity search on the resulting Job2Vec representations.

AutoencoderFAISSNLP

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Transformer‑Based Denoising AutoEncoder (TSDAE) for Job Description Embeddings (Job2Vec)

JD Tech Talk

Oct 30, 2019 · Artificial Intelligence

Solution Overview for the Scientific Paper Recommendation Matching Competition

This article presents a comprehensive solution to a competition that requires matching description paragraphs with the three most relevant papers from a 200,000‑paper corpus, detailing background, task definition, evaluation metrics, modeling strategy, and core algorithms such as SIF, InferSent, Bi‑LSTM, and BERT.

BERTNLPcompetition

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Solution Overview for the Scientific Paper Recommendation Matching Competition