Visual facet search engine

Abstract

A visual facet search engine utilizes a machine learned model for identifying an image for a facet option. Specifically, the engine receives a search image as a search query at a search engine. Further, the engine identifies search results for the search query based on search image features extracted from the search image. The search results comprise item listings associated with item listing images. In addition, the engine determines facets and facet options for the search image based on the search results. Further, the engine employs the machine learned model to identify an item listing image among the item listing images. Furthermore, the engine provides the item listing image for display as selectable facet option for navigating the search results.

What is claimed is: 
     
         1 . A computer-implemented method for a visual facet search, the method comprising:
 receiving a search image as a search query at a search engine;   identifying search results for the search query based on search image features extracted from the search image, the search results comprising item listings associated with item listing images;   determining facets for the search image based on the search results, each facet comprising facet options;   employing a machine learned model to identify an item listing image among the item listing images based on an item feature prominence score determined by the machine learned model, the item feature prominence score associated with an item feature of the item listing image and indicating a relative prominence of the item feature within the item listing images; and   providing for display at the search engine the item listing image identified by the machine learned model as a first facet option included within a first set of facet options for a first facet determined for the search image.   
     
     
         2 . The method of  claim 1 , further comprising:
 providing for display a second set of facet options for a second facet determined for the search image, a second facet option comprising a second item listing image from the item listing images of the item listings;   receiving a selection of the first facet option of the first facet; and   changing the second item listing image of the second facet option to another item listing image from the item listing images of the item listings based on the selection.   
     
     
         3 . The method of  claim 2 , wherein the second item listing image is changed based on the second item listing image not including the item feature associated with the first facet option, and wherein the another item listing image comprises the item feature associated with the first facet option, the another item listing image being selected based on a second item feature prominence score determined by the machine learned model for a second item feature associated with the second facet option. 
     
     
         4 . The method of  claim 2 , further comprising:
 determining an order for presenting the first set of facet options and the second set of facet options;   displaying the first set of facet options and the second set of facet options in the order determined; and   rearranging the order of the first set of facet options and the second set of facet options in response to receiving the selection of the first facet option.   
     
     
         5 . The method of  claim 1 , wherein the machine learned model determines the item feature prominence score based on a background size of the item listing image relative to an item feature size of the item feature. 
     
     
         6 . The method of  claim 5 , wherein the machine learned model determines the item feature prominence score based on an image recognition confidence for the item feature within the item listing image. 
     
     
         7 . The method of  claim 1 , wherein determining the facets based on the search results further comprises:
 identifying a set of facets available for the search results; and   selecting a subset of facets from the set of facets based on a user interaction history, wherein the subset of facets is provided as the facets for the search image.   
     
     
         8 . The method of  claim 1 , the method further comprising:
 employing the machine learned model to identify a second item listing image based on the second item listing image having a greatest item feature prominence score for a second item feature within a plurality of the item listing images; and   providing for display at the search engine the second item listing image identified by the machine learned model as a second facet option included within a second set of facet options for a second facet determined for the search image.   
     
     
         9 . One or more computer storage media storing computer-readable instructions that when executed by a processor, cause the processor to perform operations for providing an item option, the operations comprising:
 receiving search results comprising item listing images associated with item listings in response to using a search image as a search query at a search engine;   selecting a subset of the search results based on an image quality score determined by a background feature of each item listing image of the item listing images;   employing a machine learned model to determine item feature prominence scores for the item listing images of the subset of the search results, the item feature prominence scores associated with an item feature of the item listing images of the subset, the item feature prominence scores indicating a relative prominence of the item feature within the item listing images;   identifying an item listing image based on an item feature prominence score of the item listing image; and   providing for display at the search engine the item listing image identified by the machine learned model as a first facet option included within a first set of facet options for a first facet determined for the search image.   
     
     
         10 . The media of  claim 9 , further comprising:
 determining the first set of facet options for the first facet based on user histories of textual refinements of prior search queries at the search engine.   
     
     
         11 . The media of  claim 10 , wherein the item feature prominence score of the item listing image is determined by the machine learned model, and the item listing image is selected based on the user interaction histories from the prior search queries at the search engine. 
     
     
         12 . The media of  claim 9 , wherein the item feature prominence score of the item listing image is determined by the machine learned model based on a spatial resolution and a number of independent pixel values per unit length of the item listing image. 
     
     
         13 . The media of  claim 9 , wherein the first set of facet options is displayed in an order, the order based on user purchase histories from the prior search queries at the search engine. 
     
     
         14 . The media of  claim 9 , further comprising:
 receiving a selection of the first facet option of the first facet;   changing the item listing images of a second set of facet options that were not selected to modified item listing images based on the item listing images of the second set of facet options not having the item feature of the first facet option selected; and   changing the ordered display of the second set of facet options having the modified item listing images based on the selection of the first facet option and the user histories of the textual refinements.   
     
     
         15 . A system for a visual facet search, the system comprising:
 at least one processor; and   one or more computer storage media storing computer-readable instructions that when executed by the at least one processor, cause the at least one processor to perform operations comprising:
 provide a search image as a search query at a search engine; 
 receive a search results page from the search engine in response to the search query, the search results page comprising a set of facet options for a facet, the set of facet options being presented as item listing images of item listings related to the search query, wherein each item listing image for each facet option of the set of facet options is selected based on an item feature prominence score determined by a machine learned model, wherein item feature prominence scores are associated with an item feature of the facet, and wherein the item listing image is selected based on the item feature prominence score being greater than other item feature prominence scores of other item listing images of the item listings; 
 select a facet option from the set of facet options; and 
 receive from the search engine a subset of the item listings, each item listing of the subset of the item listings comprising the item feature associated with the facet option selected. 
   
     
     
         16 . The system of  claim 15 , wherein the subset of the item listings are presented on a user interface in a order determined from user interaction histories from prior search queries at the search engine, and wherein the operations further comprise:
 receive an ordered view of the set of facet options each having an item listing image selected based on the item feature prominence score determined by the machine learned model and corresponding to the item feature; and   receive a different item listing image for one facet option of the set of facet options after the selection of the facet option, the different item listing image determined by the machine learned model based on the different item listing image having the item feature of the facet option selected.   
     
     
         17 . The system of  claim 16 , wherein the different item listing image is determined by the machine learned model based on user purchase histories from the prior search queries at the search engine. 
     
     
         18 . The system of  claim 15 , wherein the item feature prominence score of an item listing image of the facet option selected is determined by the machine learned model based on a spatial resolution and a number of independent pixel values per unit length of the item listing image. 
     
     
         19 . The system of  claim 15 , wherein the machine learned model determines the item feature prominence score based on a comparison of a background size of the item listing image relative to the item feature within the item listing image. 
     
     
         20 . The system of  claim 15 , the operations further comprising, in response to selecting the facet option, receive an ordered view of the set of facet options, each facet option in the set of facet options having a different item listing image based on item feature prominence scores of the different item listing image determined by the machine learned model, the ordered view displayed based on user purchase histories from prior search queries at the search engine.