Abstract: Described herein are embodiments for improving search engine results of listings of For Sale Objects (FSOs). A search engine may be improved by implementing rules that resolve ambiguity between listings for different (FSOs) that match the same search inputs. An unsupervised machine learning module may evaluate candidate rules and identify improvements that may not be obvious to a human evaluator. An ecommerce site that combines the improved search engine with the unsupervised machine learning module may dynamically evaluate search results using different candidate rules and iteratively improve search results.

Abstract: Described herein are embodiments for assisting in creating a listing for a For Sale Object (FSO). An item name suggestion module receives seller input and provides suggested entries for the listing to help the seller describe the FSO more accurately and consistently. A hierarchical database provides a structure for ordering suggested entries, with the structure ordered based on scores. The scores are based on rules that relate item characteristics and take into account rankings of those item characteristics with respect to one another. Metadata tags that are used by the online merchandise platforms can be identified and included in the listing, even if a seller is not familiar with the metadata tags. The hierarchical database also connects or associates item characteristics in groups that describe specific FSO. The connections can help to optimize search results as the listing is completed by the seller.