Customer-obsessed science

Traditional recommendation systems (RecSys) face critical challenges, including limited semantic understanding of user preferences, lack of explainability, and poor adaptability to evolving user needs. These limitations hinder their ability to provide personalized, context-aware recommendations and can erode user trust. Recent work has explored the use of Large Language Models (LLMs) to address these challenges

Multilingual information retrieval has emerged as powerful tools for expanding knowledge sharing across languages. On the other hand, resources on high quality knowledge base are often scarce and in limited languages, therefore an effective embedding model to transform sentences from different languages into a feature vector space same as the knowledge base language becomes the key ingredient for cross

Online randomized controlled experiments (A/B tests) measure causal changes in industry. While these experiments use incremental changes to minimize disruption, they often yield statistically insignificant results due to low signal-to-noise ratios. Precision improvement (or reducing standard error) traditionally focuses on trigger observations - where treatment and control outputs differ. Though effective

In this paper, we present CACHE-ED, a novel framework for document entity extraction that combines the power of large language models (LLMs) with graph-based document representations, caching mechanisms, and an actor-critic multi-agent architecture. Our approach addresses the inefficiencies and inaccuracies that are common in extracting structured information from documents, particularly in templated formats

Creating authentic digital twins of mobile users through realistic user behavior simulation is critical to truly understand and anticipate customer needs at scale. Toward this, we introduce Digital TwIns of MObile User (TIMO), an end-to-end framework for high-fidelity mobile user simulation that addresses four key limitations in existing approaches: subjective decision-making diversity, scalable experience