e-commerce retrieval

Kuaishou Tech

Oct 31, 2023 · Artificial Intelligence

Kuaishou’s Nine Accepted Papers at ACM MM 2023: Summaries and Links

This article presents concise English summaries of nine Kuaishou research papers accepted at ACM MM 2023, covering topics such as no‑reference video quality assessment, adaptive video quality models, blind image super‑resolution, audio‑visual‑language transfer learning, motion‑aware video diffusion, large‑scale e‑commerce retrieval, and interactive segmentation.

AIImage Super-Resolutionaudio-visual language

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Kuaishou’s Nine Accepted Papers at ACM MM 2023: Summaries and Links

Alimama Tech

Oct 12, 2022 · Artificial Intelligence

Decoupled Graph Neural Networks for Large-Scale E-commerce Retrieval

Decoupled Graph Neural Networks (DC‑GNN) improve large‑scale e-commerce ad recall by separating graph processing from CTR prediction, using multi‑task pretraining (edge prediction + contrastive learning), efficient deep linear aggregation, and a dual‑tower CTR model, achieving higher efficiency and performance on billions‑scale data.

CTR predictionDecoupled ArchitectureGraph Neural Networks

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Decoupled Graph Neural Networks for Large-Scale E-commerce Retrieval

DataFunSummit

Mar 16, 2022 · Artificial Intelligence

Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Joint Index Training

This article presents JD's end‑to‑end semantic search recall pipeline, covering multi‑stage recall, a dual‑tower embedding model with multi‑head attention, a heterogeneous graph neural network (SearchGCN), a transformer‑based synonym generation system, and a joint index‑training approach that integrates product quantization to improve recall accuracy and efficiency.

deep learningdual-tower modele-commerce retrieval

0 likes · 17 min read