International Business Machines Corporation

Ebay, Incorporated

Verizon Patent and Licensing Inc.

Oath Inc.

Yahoo Assets LLC

Verizon Media Inc.

Ido Guy

Alexander Nus

Dan S Pelleg

Arnon Dagan

David Carmel

Boaz Mizrachi

Daniel Lee Hurwitz

Yoelle Smadja Maarek

Jeffrey Bonforte

Michal Jacovi

Idan Szpektor

Vladimir Soroka

Avihai Mejer

Kira Radinsky

Gilad Barkai

Avinoam Omer

Einav Itamar

Fiana Raiber

Raphael Bryl

Itai Turbahn

Noa Shimoni Barzilai

Eyal Sonsino

Yan Radovilsky

Slava Novgorodov

Gadi Mikles

Idan Schwartz

Gilad Kutiel

Tal Steier

Lisa Seacat DeLuca

Haggai Roitman

Roy Levin

Yosi Mass

Inbal Ronen

Shai Erera

Dafna Sheinwald

Ohad Greenshpan

Anat Hashavit

Eran Belinsky

Elad Shahar

Erel Uziel

Viatcheslav Novgorodov

Alexander Binyamin Sagiv Sonnenberg

Methods, systems, and media for lot classification are disclosed. In one example, a classification system for identifying lot listings receives a description for a listing in a publication system, identifies a string in the listing, identifies a quantity word or digit in the string, and converts an identified quantity word into digit form. A normalized string is tokenized to produce tokens, the tokenizing of the normalized string including splitting the normalized string into a series of substrings using a sequence of delimiters. For each substring, an additional split is performed by separating any digit from any other adjacent character, unless that character is another digit, and maintaining an internal character order of each split substring to produce a flattened list of tokenized tokens.

Automatic lot classification

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

Methods, systems, and computer storage media for providing supplementary item information on communications-based compatibility features for a primary item in an item listing platform. In particular, communications-based compatibility features are extracted from user communications about the primary item to identify additional features of the item. In operation, a supplementation engine of an item listing platform, accesses user communications for the primary item. Supplementary item information is generated for the item, where the supplementary item information includes a communications-based compatibility feature associated with the item. The communications-based compatibility feature indicates that the primary item is compatible with the secondary item, a non-standard feature (e.g., a feature not provided in a feature set of the primary item), or a demographic feature (e.g., age). The supplementary item information is provided to cause presentation of a user interface element corresponding to the communications-based compatibility feature on a user interface of the item listing platform.

Search system, media, and method for providing communications-based compatibility features

Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing. Execution of the disclosed systems and methods turns a tedious manual chore into an automated, software-driven process. The disclosed systems and methods utilizes a novel, computerized learn-to-rank (LTR) algorithm for identifying the most engaging, socially driven photos by: (a) grouping near-duplicate photos; (b) selecting a representative photo for sharing per group; and (c) ranking of the groups by their likelihood to contain a 'shareable' photo.

Computerized system and method for automatically determining and providing digital content within an electronic communication system

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

Various methods and systems for processing web crawling queries using a web crawling prioritization model based on classification operation performance. A classification operation for organizing products in a product listing platform is accessed. A web crawling engine is accessed for the classification operation. The web crawling engine operates based on a web crawling query prioritization model that supports determining web crawling priority scores that indicate a predicted performance improvement for classification operations executed with known data and web crawled data to be retrieved from executing a web crawling query operation. Using the web crawling prioritization model, a web crawling priority score is determined for a web crawling query for the corresponding classification operation. The classification operation is associated with a product in a product listing platform and known data for the product. Based on the web crawling priority score, the web crawling query is executed to identify web crawled data.

Search system for providing web crawling query prioritization based on classification operation performance

One or more computing devices, systems, and/or methods for generating a set of tips for an entity are provided. For example, users may create user generated content describing an entity, such as a user review for a consumer good, a location, an event, etc. Because a user may be unable to read and digest all of the user reviews for the entity, the user may merely read a few user reviews, and thus miss out on useful information. Accordingly, tip templates, indicative of how tips are linguistically/grammatically constructed, are applied to the user reviews to automatically extract a set of tips for the entity (e.g., 'make sure to bring a rain jacket'). The set of tips may be filtered to remove undesirable tips, ranked based upon usefulness, and/or diversified to remove redundant tips. In this way, a set of useful tips may be provided to the user.

Tip generation

Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. A novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing is provided. Execution of the disclosed framework turns a tedious manual chore into an automated, software-driven process. The disclosed framework utilizes a novel, computerized learn-to-rank (LTR) algorithm for identifying the most engaging, socially driven photos by: (a) grouping near-duplicate photos; (b) selecting a representative photo for sharing per group; and (c) ranking of the groups by their likelihood to contain a 'shareable' photo.

A computer implemented method, a computerized system and a computer program product for detecting scalable trends in a personalized search context. The computer implemented method comprising obtaining search results matching a search query, wherein the search query is a query defined by a user, wherein the search results comprise occurrences of events, wherein each occurrence is associated with a timestamp. The method further comprises calculating, by a processor, in response to obtaining the search results, trend scores for the events based on the occurrences comprised by the search results. The method further comprises providing an output to the user based on the trend scores.

Scalable trend detection in a personalized search context

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Haifa, Israel

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