multi‑modal | BestHub

Bilibili Tech

Jan 21, 2025 · Artificial Intelligence

Accelerating Large Model Inference: Challenges and Multi‑Level Optimization Strategies

The article outlines how exploding LLM sizes create compute, memory, and latency bottlenecks and proposes a full‑stack solution—operator fusion, high‑performance libraries, quantization, speculative decoding, sharding, contiguous batching, PageAttention, and specialized frameworks like MindIE‑LLM—to dramatically boost inference throughput and reduce latency, while highlighting future ultra‑low‑bit and heterogeneous hardware directions.

Hardware OptimizationInference Accelerationcontinuous batching

0 likes · 21 min read

Accelerating Large Model Inference: Challenges and Multi‑Level Optimization Strategies

Sohu Tech Products

Nov 27, 2024 · Artificial Intelligence

RAG Technology and Practical Application in Multi-Modal Query: Using Chinese-CLIP and Redis Search

The article explains how Retrieval‑Augmented Generation (RAG) outperforms direct LLM inference by enabling real‑time knowledge updates and lower costs, and demonstrates a practical multi‑modal RAG pipeline that uses Chinese‑CLIP for vector encoding, various chunking strategies, and Redis Search for fast vector storage and retrieval.

Chinese CLIPChunkingLLM

0 likes · 17 min read

RAG Technology and Practical Application in Multi-Modal Query: Using Chinese-CLIP and Redis Search

AntTech

Oct 30, 2023 · Artificial Intelligence

AntM2C: A Large-Scale Multi‑Scenario Multi‑Modal CTR Prediction Dataset from Alipay

AntM2C is a publicly released, billion‑sample click‑through‑rate (CTR) dataset covering five distinct Alipay business scenarios, providing both ID and rich multi‑modal (text and image) features to enable comprehensive evaluation of multi‑scenario, cold‑start, and multi‑modal CTR models at industrial scale.

Large Scalectrdataset

0 likes · 14 min read

AntM2C: A Large-Scale Multi‑Scenario Multi‑Modal CTR Prediction Dataset from Alipay

DataFunTalk

Jan 21, 2023 · Artificial Intelligence

Challenges and Best Practices in Recommendation Systems – Expert Interview

This interview with three recommendation‑system experts explores the technical architecture, data sources, feature engineering, recall and ranking strategies, evaluation metrics, cold‑start solutions, and practical difficulties, offering actionable insights to avoid common pitfalls in real‑world recommender deployments.

Cold StartFeature EngineeringRanking

0 likes · 15 min read

Challenges and Best Practices in Recommendation Systems – Expert Interview

Alimama Tech

Jun 15, 2022 · Artificial Intelligence

Multi-modal Multi-query Search Session Modeling with Heterogeneous Graph Neural Networks

The paper introduces MUVCOG, a heterogeneous graph neural network that models multi‑modal, multi‑query search sessions on Mobile Taobao by jointly learning attention‑based global and hierarchical local views through contrastive pre‑training, yielding universal session embeddings that markedly improve CTR prediction, query recommendation, and intent classification.

Pretrainingcontrastive learninge-commerce

0 likes · 15 min read

Multi-modal Multi-query Search Session Modeling with Heterogeneous Graph Neural Networks

DataFunSummit

Jan 19, 2022 · Artificial Intelligence

Feizhu Information Flow Content Recommendation: Architecture, Cold-Start Strategies, Multi-Modal Understanding, and Ranking Mechanisms

This article presents a comprehensive overview of Feizhu's information‑flow recommendation system, detailing its mixed‑material architecture, cold‑start recall and coarse‑ranking techniques, multi‑modal pre‑training and fine‑tuning, fine‑ranking with user‑state gating, and tiered traffic‑flow mechanisms for content delivery.

Cold StartRankingTravel

0 likes · 17 min read

Feizhu Information Flow Content Recommendation: Architecture, Cold-Start Strategies, Multi-Modal Understanding, and Ranking Mechanisms

DataFunSummit

Oct 10, 2021 · Artificial Intelligence

Advances in Knowledge Graph Construction and Applications at Alibaba's AliMe

This article presents Alibaba's AliMe team’s year‑long progress on knowledge graphs, covering the basics of knowledge graphs, domain‑specific and multi‑modal graph construction techniques, practical e‑commerce applications such as dialogue‑driven recommendation, virtual‑anchor script generation, and key takeaways for future research.

Artificial Intelligencee-commerceentity extraction

0 likes · 24 min read

Advances in Knowledge Graph Construction and Applications at Alibaba's AliMe