Ebay, Incorporated

Arnon Dagan

Ido Guy

Alexander Zhicharevich

Alexander Nus

Daniel Lee Hurwitz

Einav Itamar

Avinoam Omer

Yan Radovilsky

Giora Simchoni

Raphael Bryl

Gadi Mikles

Noa Shimoni Barzilai

Slava Novgorodov

A query modality recommendation system provides recommendations to use a particular query modality based on one or more categories of search results for a search query. Upon receiving a search query in a first query modality at a search engine, the query modality recommendation system determines to recommend use of a second query modality based on one or more categories of the search results. For example, the first query modality may be a textual query and the second query modality may be an image query. In aspects, recommending use of the second query modality comprises comparing a first search performance of the one or more categories for the first query modality in historical search queries to a second search performance of the one or more categories for the second query modality in the historical search queries.

Query modality recommendation for e-commerce search

A visual search recommendation engine utilizes an image quality indication model for a visual search recommendation. Specifically, the visual search recommendation engine receives a search image as a search query at a search engine. The visual search recommendation engine provides the search image as an input into the image quality indication model, which is trained to output an image quality indication based on image aspects of the search image. A plurality of images are identified from an image corpus. The visual search recommendation engine determines an image similarity based on a comparison between the plurality of images from the image corpus and the search image. The image quality indication and the image similarity indicate a search query performance for the search image. A first image exceeding the search query performance is identified. The first image is provided for display at the search engine.

Visual quality performance predictors

Disclosed are systems, methods, and non-transitory computer-readable media for automatic image selection for online product catalogs. An image selection system gathers feature data for images of an item included in listings posted to an online marketplace. The image selection system uses the feature data as input in a machine learning model to determine probability scores indicating an estimated probability that each image is suitable to represent the item. The machine learning model is trained based on a set of training images of the item that have been labeled to indicate whether they are suitable to represent the image. The image selection system compares the probability scores and selects an image to represent the item as a stock image based on the comparison.

Automatic image selection for online product catalogs

Systems and methods to select a product title are described. The system identifies a set of item listings respectively describing items being offered for sale on a network-based marketplace. Each item listing includes a product identifier that matches and is not associated with a product title on the network-based marketplace. Each item listing also includes an item title. The system extracts feature values from the item listings and processes the feature values. The system evaluates the feature values to adopt a product title from an item title included in the set of item titles. The system generates a product user interface including the product title. Finally, the system communicates the product user interface, over a network, for display on a client machine. The product user interface includes the product title.

System, method, and medium to select a product title

A browse node page is addressed by its path. As a result, a web crawler may be more likely to find the browse node than the corresponding parameter-based search page. The browse node may be further distinguished from a search result page by the use of a title or header metatag that indicates information about the browse node and distinguishes the browse node from a general search result page. The number of combinations of keywords, categories, and key-value pairs makes the creation of a browse node for every possible combination prohibitive in all but the simplest applications. Methods and systems to identify which search result pages should be converted to browse nodes are disclosed herein.

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Kfar Vitkin, Israel

Arnon Dagan