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18 papers from Amazon Science on Recommendation & Information Retrieval
In a world of rapidly expanding agent capabilities, a new retrieval-based approach keeps accuracy high and costs low, outperforming traditional methods by a significant margin.
MemTree3D slashes the computational cost of 3D question answering, boosting performance while avoiding the inefficiencies of traditional visual search methods.
GenRec shows that LLM-backed recommenders can outperform traditional models with significantly less training data, revolutionizing the recommendation landscape.
By integrating fresh and delayed signals, this approach boosts viewer engagement and revenue while reducing model complexity by nearly 42%.
Connectivity in multi-agent collaborative filtering can dramatically alter attack outcomes, revealing unexpected vulnerabilities and defense strategies.
RecRec reveals that decoupling reasoning from prediction can significantly enhance sequential recommendation performance, breaking free from the constraints of fixed-dimensional states.
Bi-NAS boosts recommendation accuracy while transforming user explanations into clear, personalized insights that enhance trust and engagement.
Advanced RAG methods like GraphRAG and Agentic RAG can reduce token usage by up to 53%, but they don't always enhance generation quality as expected.
Massive multimodal models like Qwen and CLIP excel at information retrieval, but their sheer size makes them impractical – this workshop tackles the efficiency gap.
LLMs can achieve better zero-shot product ranking with 57% less token usage by reasoning over structured attribute graphs instead of raw text.
RAG systems are stuck in a factual echo chamber, ignoring the rich tapestry of opinions that shape real-world understanding.
Prime Video's new anomaly detection system spots real incident-related services missed by traditional load testing, proving that synthetic traffic can't always predict live event behavior.
Recommending popular items isn't always what users want: SPREE steers sequential models to align with individual users' preferences for popular or niche content, improving recommendations.
LLM-generated survey responses can be statistically accurate yet still miss the option most preferred by humans, highlighting a critical flaw in current evaluation methods.
LLM-based recommender systems can trigger users' personal trauma, phobias, or self-harm history, but a new framework cuts these safety violations by 96.5% while maintaining recommendation quality.
An end-to-end system extracts funny scenes from movies with 87% accuracy, opening new avenues for automated content repurposing.
Give new e-commerce products a warm start by borrowing behavioral signals from their substitutes, boosting search relevance and product discovery.
Stop hand-rolling your multi-task learning to rank models: DeepMTL2R provides a ready-to-use framework with 21 SOTA algorithms and Pareto-optimal optimization.
