Search papers, labs, and topics across Lattice.
9 papers from Amazon Science on Recommendation & Information Retrieval
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.
