Abstract: A recommendation system identifies resale goods corresponding to retail goods being viewed by a user on a retail goods website. Retail goods metadata and images are extracted from the retail goods website using heuristics and LLM-based methods. The extracted retail goods data undergoes ML model-based product category classification, intelligent image cropping, and color detection. Vector embeddings are generated for images and text using ML models and compared to resale goods vector embeddings stored in a vector database. Multiple result sets are retrieved, fused, and re-ranked using LLM-based and preference-aware re-ranking. A data pipeline continuously loads, cleans, and processes resale goods inventory from resale goods websites.

Abstract: A system and method for an integrated subsystem that identifies resale consumer goods available for sale whereby picture or text descriptions of items of firsthand consumer goods may be used as an input or search query where a user may enter size, style, and color parameters for clothing and fashion items. The plug-in uses a web crawler to maintain a database of resale consumer goods for sale and may isolate a single clothing item from a clothed person to perform a search query for resale versions of that item as well as provided improved specifically tailored results based on what the user is looking for at that specific time.