Abstract: Object search techniques for digital images are described. In the techniques described herein, semantic features are extracted on a per-object basis form a digital image. This supports location of objects within digital images and is not limited to semantic features of an entirety of the digital image as involved in conventional image similarity search techniques. This may be combined with indications a location of the object globally with respect to the digital image through use of a global segmentation mask, use of a local segmentation mask to capture post and characteristics of the object itself, and so on.

Abstract: A search engine is configured to return search results with increased diversity among the search results. When a query is received from a client device, items are identified as search results for the query and grouped into categories. The categories are also grouped into classes. A user interface is generated with items grouped together by category and categories grouped together by class. The user interface is returned to the client device as a response to the query.

Abstract: Object search techniques for digital images are described. In the techniques described herein, semantic features are extracted on a per-object basis form a digital image. This supports location of objects within digital images and is not limited to semantic features of an entirety of the digital image as involved in conventional image similarity search techniques. This may be combined with indications a location of the object globally with respect to the digital image through use of a global segmentation mask, use of a local segmentation mask to capture post and characteristics of the object itself, and so on.

Abstract: A search engine is configured to return increased diversity results based on past user interactions with search results. For a given query, historical data is analyzed to generate an item score describing a past quantity of users that navigated to a given page of an item. The historical data can further be used to generate a category score describing a past quantity of users that navigated to a given category of items. The category of items can be analyzed to generate a diversity score describing their diversity with respect to each other. Results for the given query can be arranged using items scores, category scores, and diversity scores.

Abstract: A search engine is configured to return increased diversity results based on past user interactions with search results. For a given query, historical data is analyzed to generate an item score describing a past quantity of users that navigated to a given page of an item. The historical data can further be used to generate a category score describing a past quantity of users that navigated to a given category of items. The category of items can be analyzed to generate a diversity score describing their diversity with respect to each other. Results for the given query can be arranged using items scores, category scores, and diversity scores.