negative sampling

DataFunSummit

Jul 30, 2022 · Artificial Intelligence

Graph Link Prediction Techniques, Self‑Developed GNN Models, and Applications in Risk Control

This article reviews graph link prediction problems, categorizes existing methods from heuristics to GNN‑based approaches, introduces several self‑designed neighborhood attention networks and adversarial negative‑sampling strategies, discusses pairwise ranking objectives, reports OGB competition results, and explores practical risk‑control applications.

AIGraph Neural Networksgraph link prediction

0 likes · 15 min read

Graph Link Prediction Techniques, Self‑Developed GNN Models, and Applications in Risk Control

DataFunTalk

Apr 21, 2022 · Artificial Intelligence

Solving Cold‑Start in Recommender Systems: The DropoutNet Approach

This article explains why cold‑start is a critical challenge for recommender systems, outlines four practical strategies—generalization, fast data collection, transfer learning, and few‑shot learning—and then details the DropoutNet model, its end‑to‑end training, loss functions, negative‑sampling techniques, and open‑source implementation.

Cold StartDropoutNetembedding

0 likes · 21 min read

Solving Cold‑Start in Recommender Systems: The DropoutNet Approach

Tencent Cloud Developer

Apr 11, 2022 · Artificial Intelligence

Recall Module in Recommendation Systems: Multi-Path Retrieval and Optimization

The recall module in recommendation systems retrieves thousands of items from massive pools using parallel non-personalized and personalized paths—such as hot-item, content-based, behavior-based, and deep-model recall—prioritizing coverage and low latency while addressing challenges like hard-negative sampling, selection bias, objective alignment, and channel competition to feed downstream ranking.

AIMulti-Path RetrievalRecommendation systems

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Recall Module in Recommendation Systems: Multi-Path Retrieval and Optimization

Baidu Geek Talk

Nov 29, 2021 · Artificial Intelligence

Pretrained Models for First-Stage Information Retrieval: A Comprehensive Review

This comprehensive review by Dr. Fan Yixing surveys how pretrained language models have transformed first‑stage information retrieval, tracing the shift from traditional term‑based methods to neural sparse, dense, and hybrid approaches, and discussing key challenges such as hard‑negative mining, joint indexing‑representation learning, and generative‑discriminative training.

Hybrid RetrievalNeural IRSparse Retrieval

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Pretrained Models for First-Stage Information Retrieval: A Comprehensive Review

Tencent Advertising Technology

Nov 26, 2020 · Artificial Intelligence

Representative Negative Instance Generation for Online Ad Targeting (RNIG)

Researchers from Tencent Ads and Tsinghua University introduced a novel Generative Adversarial framework, the Representative Negative Instance Generator (RNIG), which creates high‑quality representative negative samples from exposure data to mitigate data imbalance and selection bias, achieving superior performance on CIKM‑2020 ad targeting benchmarks.

Generative Adversarial NetworksRecommendation systemsad targeting

0 likes · 8 min read

Representative Negative Instance Generation for Online Ad Targeting (RNIG)

Sohu Tech Products

May 27, 2020 · Artificial Intelligence

Overview of Embedding Methods: From Word2Vec to Item2Vec and Dual‑Tower Models in Recommendation Systems

This article provides a comprehensive overview of embedding techniques, explaining their role in deep learning recommendation systems, detailing Word2Vec and its Skip‑gram model with negative sampling and hierarchical softmax, and extending the discussion to Item2Vec and dual‑tower architectures for item representation.

Recommendation systemsWord2Vecdeep learning

0 likes · 15 min read

Overview of Embedding Methods: From Word2Vec to Item2Vec and Dual‑Tower Models in Recommendation Systems