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leeloolee

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reacted to singhsidhukuldeep's post with 👀 about 22 hours ago
Exciting breakthrough in e-commerce recommendation systems! Walmart Global Tech researchers have developed a novel Triple Modality Fusion (TMF) framework that revolutionizes how we make product recommendations. >> Key Innovation The framework ingeniously combines three distinct data types: - Visual data to capture product aesthetics and context - Textual information for detailed product features - Graph data to understand complex user-item relationships >> Technical Architecture The system leverages a Large Language Model (Llama2-7B) as its backbone and introduces several sophisticated components: Modality Fusion Module - All-Modality Self-Attention (AMSA) for unified representation - Cross-Modality Attention (CMA) mechanism for deep feature integration - Custom FFN adapters to align different modality embeddings Advanced Training Strategy - Curriculum learning approach with three complexity levels - Parameter-Efficient Fine-Tuning using LoRA - Special token system for behavior and item representation >> Real-World Impact The results are remarkable: - 38.25% improvement in Electronics recommendations - 43.09% boost in Sports category accuracy - Significantly higher human evaluation scores compared to traditional methods Currently deployed in Walmart's production environment, this research demonstrates how combining multiple data modalities with advanced LLM architectures can dramatically improve recommendation accuracy and user satisfaction.
upvoted a paper 14 days ago
GUI Agents: A Survey
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leeloolee's activity

upvoted an article about 1 month ago
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LLaVA-o1: Let Vision Language Models Reason Step-by-Step

By mikelabs
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upvoted an article about 2 months ago
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Introducing Observers: AI Observability with Hugging Face datasets through a lightweight SDK

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