Abstract: Systems and methods provide determining listings of items based on similarities at least among items and queries in an online shopping system. In particular, the systems and methods determine similarities among items, users, product, messages, reviews, and queries, based on a combination of a machine learning model and similarity index data. The machine learning model (e.g., a Transformer model and a neural network model) generates embedded vector representation of items, queries, and other data in the online shopping systems. The machine learning model may be pre-trained based at least on data associated with items in the online shopping system, and fine-tuned based on a variety of mappings of similarities: item-to-item, user-to-item, query-to-item, and the like. The similarity index data include k-Nearest Neighbor index data for determining items within a range of similarity based on a receive query.