Xerox Corporation

Ebay, Incorporated

Palo Alto Research Center Inc.

Apple Inc.

The Scripps Research Institute

Intel Corporation

David Goldberg

Roy Want

Marshall Bern

Anthony George LaMarca

Mark D Weiser

Todd A Cass

Xiaoyuan Wu

Yuanjie Liu

Michael Ching

Dingxian Wang

James Douglass Thornton

Kenneth P Fishkin

Anuj Uday Gujar

Daniel Miranda

Daniel H Greene

Beverly L Harrison

William James Rucklidge

Gyanit Singh

Davide Libenzi

David A Nichols

Justin Nicholas House

Nish Parikh

Jonas Oscar Klink

Elizabeth D Mynatt

Huai-ter Chong

Narayanan Seshadri

Amit Jaiswal

Stephen Anthony Neola

Jan Matthias Ruhl

Miroslav Melichar

Daniel Fain

Steven Jay Harrington

Scott Alan Elrod

Eric J Shrader

Douglas N Curry

Douglas Brian Terry

Kurt Edward Partridge

Nicolas B Ducheneaut

Koji Yatani

Mark Webster Newman

Yu Liu

Maureen C Stone

Joseph Anthony Beynon

Brent B Welch

Alpha Kamchiu Luk

Scott L Minneman

Prathyusha Senthil Kumar

Philippe J Golle

Steve R Harrison

Vinayak Agarwal

Shervin Moghaddam

Ishita Kamal Khan

Gajanan Adalinge

Ming Ye

Nadia Vase

Song Feng

Ben Chisell

Johannes Braun

Tom Pugh

Brian M Oki

Tom Wagstaff

Mark David Weiser

John R Yates, Iii

Mark W Newman

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.

Dissimilar but relevant search engine results

A categorization analysis system is provided. The categorization analysis system includes one or more hardware processors, a memory including a first plurality of listings categorized in a first target category, and a categorization analysis engine executing on the one or more hardware processors. The categorization analysis engine is configured to determine a label for each listing including performing a search on title, select a set of training listings based on the determined labels, train a first model using the set of training listings and the determined labels, the first model being a classification model configured to classify categorization of listings, identify a suspect listing categorized in the first target category, apply the suspect listing to the first model, thereby generating a categorization result for the suspect listing, the categorization result indicating miscategorization of the suspect listing, and identify the suspect listing in the memory as miscategorized.

System and method for identifying miscategorization

In various example embodiments, a system and method for retrieving database records are presented. A method includes receiving an access request for database records, identifying a primary database record corresponding to the access request, and selecting a set of secondary database records corresponding to a portion of the access request. The method determines a set of differentiating elements within records of the set of secondary database records. Each differentiating element distinguishes at least one record of the set of secondary database records from one or more of the primary database record and a portion of the records included in the set of secondary database records. The method generates a formatted search interface including a set of selectable interface icons representing one or more differentiating elements and causes presentation of the formatted search interface at a client device associated with the access request.

Database search optimizer and themed filters

In various example embodiments, a system and method for computing a query feature score that can be used by a machine learning algorithm to rank search results is described. A query is received. The assigned probabilities for the arbitrary query is retrieved. The assigned probabilities is based on a probability model for a query feature. A score is computed for the query feature based on the assigned probabilities for the arbitrary query.

System and method for computing features that apply to infrequent queries

Various methods and systems for providing query result items using an item title demand model are provided. A query is received at a search engine. Based on receiving the query, an item title demand engine is accessed. The item title demand engine operates based on an item title demand model which uses token weights, representing skip probabilities of tokens in item titles, to determine title scores for result item titles for corresponding queries. Based on accessing the item title demand engine, one or more result item titles for the query are identified from items in an item database. An identified result item title is identified based on a title score determined using the item title demand model and a highest skip probability of a token in the result item title. The one or more result item titles are communicated to cause display of the one or more result item titles.

Search system for providing query result items based on highest skip probabilities of item title tokens

Disclosed is a system comprising a computer-readable storage medium storing at least one program, and a computer-implemented method for generating search results. A data access module accesses search results data indicative of a plurality items and that is indicative of ranking values of the plurality of items. A controller module generates page data based on the search results data. The controller module selects a first item from the plurality of items. A diversification module accesses a first rule of the page data. The diversification module determines a deviance value and a reordering-cost value of a second item from the plurality of items. The controller module can select, based at least on a first combination of the deviance and the reordering-cost values of the second item, the second item for placement ahead of the first item on the page data.

Diversifying search results

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.

Disclosed are a system comprising a computer-readable storage medium storing at least one program, and a computer-implemented method for generating search results. A data access module accesses search results data indicative of a plurality items and that is indicative of ranking values of the plurality of items. A controller module generates page data based on the search results data. The controller module selects a first item from the plurality of items. A diversification module accesses a first rule of the page data. The diversification module determines a deviance value and a reordering-cost value of a second item from the plurality of items. The controller module can select, based at least on a first combination of the deviance and the reordering-cost values of the second item, the second item for placement ahead of the first item on the page data.

Techniques for determining search results based on session based refinements are presented herein. A method is disclosed that includes receiving a query in a user session, the query comprising one or more search parameters, detecting, in the user session and after receiving the query, a user event associated with a property of an item, updating a record in a table that associates the query with the property, the table comprising a plurality of records that associate the query with respective item properties, the record comprising the query, the property, and a score, and ranking search results for a subsequent query based on the associated properties indicated in the plurality of records, the subsequent query including the one or more search parameters.

Determining search results using session based refinements

Systems and methods to predict bidding behavior are described. The system identifies a listing that includes listing information that describes an item that is being auctioned on a network-based marketplace. The system further identifies bid classification information based on the number of bids received for the item. Finally, the system predicts whether no more bids are expected to be received for the item based on the classification information.

Systems and methods to predict bidding behaviour

Embodiments of a system, a method, and a computer-program product (e.g., software) that facilitates collaborative interaction with content by multiple users are described. In this system, users of a distributed group of dedicated electronic devices provide signals that indicate their current actions as they view the content. Using these signals, the system provides additional signals for display on the group of electronic devices. These additional signals provide an environment that includes the content and the current user actions, which allows the users to collaboratively interact with the content and with each other. Moreover, the multiple users may be dynamically aggregated based on their actions, which allows a social network to be established and dynamically maintained based on user activities and interests.

System for collaboratively interacting with content

Growing community of inventors

Palo Alto, CA, United States of America

David Goldberg