Paypal, Inc.

Apple Inc.

Ebay, Incorporated

Rabi Chakraborty

Manas Haribhai Somaiya

Nirveek De

Dennis Marshall

Ganesh Gopalakrishnan

Akshata Baliga

Sudeep Dasgupta

Meenal Varshney

Ayon Sinha

Chandrasekar Venkataraman

Jian J Xu

Jayasimha R Katukuri

Corey Demarco

Taylor Gerard Carrigan

Thomas M Alsina

Drew Rocky Domm

Venkat Sundaranatha

Peter Leong

Mark Hilary Levy

Rajesh Kumar

Hardik Vala

Sofia M Nikolakaki

Dimitrios Bermperidis

Natalia G Silveira

Puja Das

Simon C Janpanah

Thomas S Plagenhoef

A search engine determines a number of pages to present to a user based on one or more of a variety of factors. The search engine may send the search results to a client machine, which may present a subset of the results along with a pagination control in a scrollable interface. The pagination control may present the number of pages determined by the search engine. The search engine may also determine a number of search results to present prior to the presentation of non-scrollable user interface (UI) elements based on the same or different factors. After the amount of scrolling exceeds a threshold, a non-scrollable UI element may be displayed. Additional thresholds may exist, such that additional non-scrollable UI elements are added as the user continues to scroll.

Seamless pagination

A distribution platform interface is presented with respect to a seed item landing page. The seed item landing page includes a recommendation section. The items listed in the recommendation section are selected from a framework that is configured to provide recommendations of candidate items based on a given seed app and relationship type, and which is configured to handle multiple relationship types.

Online meta-learning for scalable item-to-item relationships

Disclosed herein is a technique for providing software application recommendations to a user of a computing device. The technique can include: (1) receiving, from the computing device, a request for at least one software application recommendation, (2) identifying, among a plurality of user profiles, a user profile associated with the user, (3) accessing a plurality of software application profiles (SAPs), wherein each SAP of the plurality of SAPs is associated with a respective software application managed by the server computing device, (4) analyzing the user profile against a subset of the plurality of SAPs to identify, among the respective software applications associated with the subset of the plurality of SAPs, at least one software application to recommend, (5) associating the at least one software application recommendation with the at least one software application, and (6) causing the computing device to display the at least one software application recommendation.

Techniques for personalizing app store recommendations

A user may submit a search query to a search engine, which may process the search query and generate a set of results. Previous actions of the user or of other users may be used to identify recommended search queries. The recommended search queries may be presented to the user along with the search results for the query. The location of the recommended search queries may be fixed, set by the user, or determined on a query-by-query basis. A user interface element operable to cause the presentation of one or more recommended search queries may be presented after a delay. After an additional or independent delay, the recommended search queries may be presented automatically. The delays may be fixed, set by the user, or determined on a query-by-query basis.

Search guidance

Methods and systems for extracting targeted data for real-time reporting are discussed. In an example, a system can include a data store, a server, and a denormalized database. The data store can maintain data created by an application. The server can be communicatively coupled to the data store. The server can include a data extraction module and a data compression module. The data extraction module can extract a subset of the data stored in the data store according to an extraction scheme. The data compression module can compress the extracted subset of the data into a set of aggregated key value pairs. The denormalized database can store the aggregated key value pairs.

Targeted multi-dimension data extraction for real-time analysis

The techniques utilize a user's recommendation data to choose a presentation template. Each presentation template includes a plurality of media asset slots, each of which is associated with a certain type of media asset. For example, each media asset slot generally may be assigned to a playlist, album, composer, or music video. Further, each of these general assignments may further specify that the playlist, album, composer, or music video be from a known artist, familiar artist, recommended artist, or unknown artist, for example. Once the appropriate presentation template has been chosen along with the recommended media content for each of the plurality of media asset slots, the media asset slots are associated with the selected media content. The presentation template is then utilized to generate a graphical user interface that may be delivered to the display of an electronic device for presentation to a user, where the order in which the recommended media content is presented via the graphical user interface corresponds to the order of the media asset slots of the chosen presentation template.

Personalized media presentation templates

Growing community of inventors

Sunnyvale, CA, United States of America

Rabi Chakraborty