Abstract: Systems and methods are directed to providing multi-level rich autosuggestions for multi-intent search queries. The system receives a query prefix entered in a search box and accesses a database of machine-learning derived intent clusters. Based on the query prefix, a top-ranking machine-learning derived intent cluster is identified, and the search user interface is updated to provide a plurality of first level autosuggestions in an autosuggestion box including a smart suggestion. The smart suggestion is a first level suggestion of the top-ranking machine-learning derived intent cluster. The system receives a selection of the smart suggestion and, in response, replaces the plurality of first level autosuggestions in the autosuggestion box with second level autosuggestions. The second level autosuggestions comprise one or more intent categories that are subsets of the smart suggestion, whereby each intent category comprises a plurality of subcategory suggestions.

Abstract: Systems and methods are directed to providing multi-level rich autosuggestions for multi-intent search queries. The system receives a query prefix entered in a search box and accesses a database of machine-learning derived intent clusters. Based on the query prefix, a top-ranking machine-learning derived intent cluster is identified, and the search user interface is updated to provide a plurality of first level autosuggestions in an autosuggestion box including a smart suggestion. The smart suggestion is a first level suggestion of the top-ranking machine-learning derived intent cluster. The system receives a selection of the smart suggestion and, in response, replaces the plurality of first level autosuggestions in the autosuggestion box with second level autosuggestions. The second level autosuggestions comprise one or more intent categories that are subsets of the smart suggestion, whereby each intent category comprises a plurality of subcategory suggestions.