COSMO: A large-scale e-commerce common sense knowledge generation and serving system at Amazon
Applications of large-scale knowledge graphs in the e-commerce platforms can improve shopping experience for their customers. While existing e-commerce knowledge graphs (KGs) integrate a large volume of concepts or product attributes, they fail to discover user intentions, leaving the gap with how people think, behave, and interact with surrounding world. In this work, we present COSMO, a scalable
Overview This paper presents a method for efficiently fine-tuning large language models (LLMs) for image-guided outfit recommendation with user preference feedback.The proposed approach, called "Decoding Style," leverages direct feedback optimization to personalize the LLM for each user's fashion preferences.The method aims to enable more accurate and personalized "Complete the Look" recommendatio
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