Amazon Technologies, Inc.

International Business Machines Corporation

Akamai Technologies, Inc.

Srikanth Thirumalai

Vijai Mohan

Grant Michael Emery

Aswath Manoharan

Mark J Tomko

Elmore Eugene Pope

Jay Parikh

Egidio Terra

Amber Roy-Chowdhury

William Edward Weihl

Mark Tsimelzon

Andrew Thomas Davis

B Anthony Joseph

Matthew K Green

Rouhollah Rahmani

Xiaoxin Yin

F Thomson Leighton

Ravi Sundaram

Matthew S Levine

Hariharan Rahul

Lisa Jane Hinegardner

Adrian Soviani

Robert D Kleinberg

Bruce Maggs

Jianhui Wu

Rizwan S Dhanidina

Andrew Dennis Hamel

Yoav O Yerushalmi

Trevor Emerson Hill

Ajamu Akinwunmi Wesley

Leo Parker Dirac

Michael C Wanderski

Daniel M Lewin

Robert Yuji Haitani

Amber Autrey Taylor

Scott Allen Mongrain

Oleg Rybakov

Aneeb Qureshi

Michael Paul Touloumtzis

Joseph Xavier

Rejith George Joseph

Rahul H Bhagat

Anne E Lewin, Legal Representative

Madhu Madhava Kurup

Shashank Gupta

Erik Warren Selberg

Adam Callahan Sanders

Matias Omar Gregorio Benitez

Brian Cameros

Jean Joseph Tavernier

Pranav Dandekar

Roshan Ram Rammohan

Prasant K Kontamsetty

Nicholas R Boyd

Yong Gang Cao

Hanumaiah Chowdary

Chee H Chew

Cory William Toedebusch

A network-based enterprise or other system that makes items available for selection to users may implement selecting user interface elements for inclusion with a search result according to item category features of prior item selections. A search request for an item may be received. An item category for the item may be identified and a user interface element type selection model for the item category may be accessed to select of user interface element types for inclusion in a display of a search result in response to the search request. The user interface element type selection model for the item category may be generated based on features of previous item selections in the identified item category. Content for the selected user interface elements may be determined and a display of the search result may be provided that includes user interface elements generated according to the selected type and identified content may be included.

System, method and non-transitory computer-readable medium for selecting user interface element types for display with a search result according to item category features of prior item selections

The selection of search results to display can be based at least in part upon an intent determined for a search query. Various intent terms can be used to specify intent or context for a search query in order to obtain a certain type of result. The intent terms can be suggested or provided to a user in such a way as to train the user as to their existence and usage. A submitted search query is analyzed to detect the intent term and determine the overall intent of the query. One or more targeted intent queries are executed against determined data sources to obtain results relevant to that intent, such as new content items for a particular topic or items that are similar to those offered by a particular store. A diverse assortment of results can then be presented to the user.

Intent determinations for content search

Techniques for improving access to and interactions with bots are described. In an example, a first bot, hosted on a computing system, may identify an action to be performed for a user associated with a computing device. The action may be identified based on a user interaction with the first bot, where the user interaction may be provided from the computing device. The first bot may select a second bot based on the action. The second bot may be hosted on a same or a different computing system. The first bot may determine, based on a previous user input to the first bot, a parameter value for an execution of the action and may send the parameter value to the second bot. In response, the first bot may receive a result of an execution of the action and may provide information about the result to the computing device.

Bot search and dispatch engine

Various embodiments of a system and method for highly scalable data clustering are described. Embodiments may include generating contexts of information from item-level data for multiple items; each context may include tokens that each represents an aggregate characteristic of items associated with that context. Embodiments may also include comparing the tokens of different contexts to determine measures of similarity between the contexts. Embodiments may also include grouping at least some of the contexts into clusters with other contexts based on the determined measures of similarity. Embodiments may include, subsequent to detecting a first context and a second context as being members of a common cluster, correcting item level-data of an item associated with the second context based on item-level data of an item associated with the first context.

System and method for highly scalable data clustering

Disclosed are various embodiments for identifying items that may achieve success and/or popularity. An approval user interface element can be associated with an item in a content page. Approval indications that correspond to user interactions with the approval user interface elements are obtained. An item score is maintained and adjusted based at least upon the approval indications received. The adjustment to the item score is based upon a number of received approval indications and/or a reputation score of users to which the approval indications correspond.

Detecting item trends

Embodiments may include a system configured to receive search session data that indicates, for each of multiple search sessions performed by a respective user, multiple search queries submitted by that user during the search session. The system may also receive search selection data that indicates, for each of multiple search queries resulting in a set of search results, a particular item selected from that set of search results by a respective user. The system may be configured to perform a co-occurrence analysis on the search data in order to generate one or more search indices that specify, for a given search query, one or more search queries determined to be related to the given search query according to the co-occurrence analysis. The system may be configured to process a client request for related queries that are related to a query of interest submitted by the user within that request.

System and method for providing query recommendations based on search activity of a user base

Techniques for determining user input on a touch screen of a user device are disclosed. In some situations, the techniques include: receiving information about a user input provided to a touch screen of a user device, the touch screen displaying two or more selectable objects, wherein each of the selectable objects, if selected, initiates a response corresponding to the selection of the object, determining a selectable object among the selectable objects that has a highest likelihood of being an object that a user intended to select with the user input, and providing a response corresponding to a selection of the determined selectable object. In one situation, a selectable object is a button or a hyperlink included in a Web page displayed on the screen of the device, and the response includes content associated with the selected button or hyperlink.

Techniques for predicting user input on touch screen devices

Extracting content from an associate website may enable a host website to gain insight into web content that are effective at driving consumers to the host website. The content extraction may involve selecting an associate website from multiple associate websites for content extraction, with the associate website including a referral link to an item for sale on the host merchant website. Content may be obtained from one or more web pages of the associate website, and at least a part of the content may be associated with the item that is listed for sale on the host website.

Reverse associate website discovery

Disclosed are various embodiments for a similarity service. Multiple samplings of user accounts are randomly selected from a pool of user accounts. Interaction history data for each of the user accounts is used to determine item similarities corresponding to each of the user account samplings. The item similarity data is aggregated to determine similar items.

Random sample aggregation similarities

Recommendations can be generated even in situations where sufficient user information is unavailable for providing personalized recommendations. Instead of generating recommendations for an item based on item type or category, a relation graph can be consulted that enables other items to be recommended that are related to the item in some way, which may be independent of the type or category of item. For example, images of models, celebrities, or everyday people wearing items of clothing, jewelry, handbags, shoes, and other such items can be received and analyzed to recognize those items and cause them to be linked in the relation graph. When generating recommendations or selecting advertisements, the relation graph can be consulted to recommend products that other people have obtained with the item from any of a number of sources, such that the recommendations may be more valuable to the user.

Object relation builder

A system and method for managing reconciled textual descriptions based on merchant quality rating are provided. Merchants providing items to an electronic marketplace submit merchant values corresponding to textual descriptions of attributes of the item. A merchant value analyzer component processes multiple merchant descriptions to select a set of textual descriptions based on a comparison of the textual descriptions and merchant quality ratings. Additionally, a merchant quality analyzer component calculates updated merchant quality ratings based on the submitted merchant values.

Managing textual descriptions based on quality rating

Disclosed are various embodiments of systems, methods and computer programs for proactive pricing. An offer to sell a product extended by a seller is maintained in a server. The offer to sell includes a plurality of asking terms and at least one selling rule authorizing a deviation from the asking terms and that is associated with the offer. A plurality of purchase offers from at least one buyer to purchase the product is maintained in the server. Each of the purchase offers specifies at least one purchase term. The purchase offers are ranked based upon a degree to which the respective purchase terms match the asking terms.

Proactive Pricing

According to aspects of the disclosed subject matter, a method for identifying a set of documents from a document corpus that are potential duplicates of a source document, is provided. A source document is obtained. A list of queries corresponding to the source document is identified. Each query in the identified list of queries is executed on the document corpus, wherein the execution of each query yields a corresponding results set identifying an ordered set of documents in the document corpus. For each document identified in each results set, a document score is generated for the identified document based on the identified document's ordinal position in its results set. A subset of the identified documents of the results set is selected according to the generated document scores that satisfy predetermined selection criteria. The selected subset of identified documents are stored or displayed.

Identifying potential duplicates of a document in a document corpus

Embodiments may include, for each item in a subset of items from a larger group of items, evaluating item description information about that item to identify a respective set of candidate phrases to be evaluated. Embodiments may also include, for each phrase in the sets of candidate phrases, generating multiple component scores based on one or more of the frequency with which that phrase occurs in the item description information for the subset of items and/or the frequency with which that phrase occurs in a corpus of item description information for the overall group of items. Embodiments may also include, for each phrase in the sets of candidate phrases, generating a respective phrase score based on the component scores generated for that phrase. Embodiments may include, based on phrase scores, selecting a subset of phrases from the sets of candidate phrases as being feature phrases for the subset of items.

System and method for identifying feature phrases in item description information

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Clyde Hill, WA, United States of America

Srikanth Thirumalai