Apple Inc.

Chandrasekar Venkataraman

Jayasimha R Katukuri

Rabi Chakraborty

Taylor Gerard Carrigan

Thomas M Alsina

Tao Wang

Drew Rocky Domm

Venkat Sundaranatha

Peter Leong

Mark Hilary Levy

Venkat Kranthi Chalasani

Venkatakrishnan S Sundaranatha

Rajesh Kumar

Sofia M Nikolakaki

Thomas S Plagenhoef

Evangelia Christakopoulou

Dimitrios Bermperidis

Natalia G Silveira

Puja Das

Moloud Shahbazi

Aravind Ragipindi

Simon C Janpanah

Hardik Vala

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

A device implementing the subject system may include at least one processor configured to obtain a first preference profile corresponding to a first user, a second preference profile corresponding to a second user, and a group preference profile corresponding to a user group that includes the first and second users. The at least one processor may be further configured to generate an aggregate preference profile based at least in part on the first preference profile, the second preference profile, and the group preference profile and to identify content items based at least in part on the aggregate preference profile. The at least one processor may be further configured to rank the content items and provide, for display on a content output device, at least one indication of at least one of the ranked content items as a recommendation for the user group.

Providing content recommendations for user groups

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

In some implementations, a computing device can improve diversity in media item recommendations. For example, the computing device can generate a satisfaction score representing an estimate of the amount satisfaction a user will experience when a candidate media item selected by the user is presented (e.g., played) by the system. The system can determine the difference in satisfaction scores for similar media items over time to determine the amount of satisfaction gained with a subsequent selection and presentation of similar candidate media items. The system can rank candidate media items based the satisfaction gain calculated for each candidate media item and select suggested media items to present to the user based on the candidate media items having the highest satisfaction gain. In some implementations, the computing device can generate satisfaction scores based on an adventurousness score calculated for the user.

Diversity in media item recommendations

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

Palo Alto, CA, United States of America

Chandrasekar Venkataraman