Adobe, Inc.

Kunal Kumar Jain

Balaji Krishnamurthy

Nikaash Puri

Mihir Naware

Kumar Mrityunjay Singh

Yaman Kumar

Hyman Chung

William Brandon George

Ashish Kumar

Deepak Pai

Lauren Dest

Joshua Butikofer

Shankar Venkitachalam

Simra Shahid

Matthew William Rozen

Sukriti Verma

Brandon Pack

Tathagata Sengupta

Vinh Ngoc Khuc

Pranjal Prasoon

Somesh Singh

Vijay Srivastava

Mihai Corlan

Anirban Basu

Timothy Chia-chi Liu

Alexander Klimetschek

Shirsh Bansal

Sandipan Karmakar

Sean Steimer

Nimish Srivastav

Florin Silviu Iordache

Horea Bacila

Umang Moorarka

Suman Basetty

Clinton Hansen Goudie-Nice

James Patrick O'Reilly

Abhinav Chauhan

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Determining user affinities for content generation applications

Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Content analytics as part of content creation

Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine. The platform continues to update the low-impression keyword model while deployed according to the sparse-data algorithm.

Keyword bids determined from sparse data

Growing community of inventors

Chennai, India

Kunal Kumar Jain