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Srihari Thyagarajan

Haleshot

https://haleshot.github.io/

AI & ML interests

AI, ML, DL, CV, RecSys

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liked a Space 12 days ago

franciszzj/Leffa

reacted to singhsidhukuldeep's post with 🚀 17 days ago

Exciting breakthrough in AI Recommendation Systems! Just read a fascinating paper from Meta AI and UW-Madison researchers on unifying generative and dense retrieval methods for recommendations. The team introduced LIGER (LeveragIng dense retrieval for GEnerative Retrieval), a novel hybrid approach that combines the best of both worlds: Key Technical Innovations: - Integrates semantic ID-based generative retrieval with dense embedding methods - Uses a T5 encoder-decoder architecture with 6 layers, 6 attention heads, and 128-dim embeddings - Processes item attributes through sentence-T5-XXL for text representations - Employs a dual-objective training approach combining cosine similarity and next-token prediction - Implements beam search with size K for candidate generation - Features an RQ-VAE with 3-layer MLP for semantic ID generation Performance Highlights: - Significantly outperforms traditional methods on cold-start recommendations - Achieves state-of-the-art results on major benchmark datasets (Amazon Beauty, Sports, Toys, Steam) - Reduces computational complexity from O(N) to O(tK) where t is semantic ID count - Maintains minimal storage requirements while improving recommendation quality The most impressive part? LIGER effectively solves the cold-start problem that has long plagued recommendation systems while maintaining computational efficiency. This could be a game-changer for e-commerce platforms and content recommendation systems! What are your thoughts on hybrid recommendation approaches?

reacted to singhsidhukuldeep's post with 👍 17 days ago

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liked a Space 12 days ago

Running on L40S

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Leffa

reacted to singhsidhukuldeep's post with 🚀👍 17 days ago

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Exciting breakthrough in AI Recommendation Systems! Just read a fascinating paper from Meta AI and UW-Madison researchers on unifying generative and dense retrieval methods for recommendations.

The team introduced LIGER (LeveragIng dense retrieval for GEnerative Retrieval), a novel hybrid approach that combines the best of both worlds:

Key Technical Innovations:
- Integrates semantic ID-based generative retrieval with dense embedding methods
- Uses a T5 encoder-decoder architecture with 6 layers, 6 attention heads, and 128-dim embeddings
- Processes item attributes through sentence-T5-XXL for text representations
- Employs a dual-objective training approach combining cosine similarity and next-token prediction
- Implements beam search with size K for candidate generation
- Features an RQ-VAE with 3-layer MLP for semantic ID generation

Performance Highlights:
- Significantly outperforms traditional methods on cold-start recommendations
- Achieves state-of-the-art results on major benchmark datasets (Amazon Beauty, Sports, Toys, Steam)
- Reduces computational complexity from O(N) to O(tK) where t is semantic ID count
- Maintains minimal storage requirements while improving recommendation quality

The most impressive part? LIGER effectively solves the cold-start problem that has long plagued recommendation systems while maintaining computational efficiency.

This could be a game-changer for e-commerce platforms and content recommendation systems!

What are your thoughts on hybrid recommendation approaches?