Adobe, Inc.

Payal Bajaj

Sumit Shekhar

Tanya Goyal

Gaurush Hiranandani

Shriram Venkatesh Shet Revankar

Balaji Vasan Srinivasan

Niyati Himanshu Chhaya

Mridul Kavidayal

Harsh Jhamtani

Sanket Vaibhav Mehta

Anandhavelu N

George Horia Galatanu

Ponnurangam Kumaraguru

Ankur Garg

Md Nadeem Akhtar

Lakshmi Shivalingaiah

Sweta Agrawal

Shriram V S Revankar

Shubham Agrawal

Priyanshu Srivastava

Abhishek Kedia

Techniques and systems for determining paywall metrics are described. In an implementation, a candidate paywall metric is created that corresponds to an increased propensity of users to engage in a paid transaction when exposed to a paywall. In this way, providers of digital content may increase the proportion of users that perform a transaction when exposed to the paywall.

Determination of paywall metrics

In some embodiments, a computing system determines, based on stored user information retrieved from a mobile user device and associated with a particular user, a location-specific affinity of the particular user for a product at a particular geographical location. The location-specific affinity indicates an interest of the particular user in the product that increases when the particular user is positioned at the particular geographical location. The computing system designs a geo-fence targeted to the particular user based on the location-specific affinity, where messages are transmitted to the mobile user device if the particular user is within a boundary of the geo-fence. The geo-fence defines a geographical area that includes the particular geographical location and that is associated with a provider of the product. The computing system causes a telecommunication server to transmit the message to the user device when the user device is positioned within the designed geo-fence.

Smart geo-fencing using location sensitive product affinity

Disclosed are various embodiments for automatically creating on a computer analytics tags for different object types of website objects in web pages with analytics tracking capability in a dynamic tag management system. In one implementation, user input is received identifying a website object for tagging in the web pages and keywords are identified based on the user input. Based on the keywords, multiple occurrences of the website object in the web are identified, wherein the multiple occurrences of the website object correspond to multiple object types. The computer automatically creates analytics tags for the website object corresponding to object types. Based on the website object, an expansion object is identified and the computer automatically creates an analytics tag for the expansion object.

Methods and systems for tag expansion by handling website object variations and automatic tag suggestions in dynamic tag management

Multiple channels of audio/video (A/V) content in a system are available to users. A channel scoring system uses a user's channel watching behavior and channel browsing behavior to generate a channel preference score for various different channels in the system. The channel watching behavior refers to which channels the user watches. The channel browsing behavior refers to features describing the user's behavior when watching A/V content provided by a channel. Examples of these features include the hour of the day when the user watches the A/V content, the number and length of A/V content provided by the channel watched in a session, device preferences of the user, and so forth. Various actions can be taken based on the generated channel preference scores, such as recommending or otherwise promoting channels to the user.

Channel preference scoring for users based on channel watching and browsing behavior

Various embodiments disambiguate users who share media content accounts to provide persona-based experience individualization. Personas correspond to commonly observed channel watching patterns among media content customers. Decomposition of the media content account into personas is achieved by analyzing many accounts, e.g., millions of accounts, on media content platforms. By analyzing accounts, a recommendation system can individualize the channel watching experience in media content accounts.

Decomposing media content accounts for persona-based experience individualization

Techniques and systems for auditing and augmenting user-generated tags for digital content are described. A corpus is generated for each tag associated with digital content to represent different aspects of the tag's definition. A hierarchy of semantic relationships between the tags is created based a corpus co-occurrence between the tags to relate at least two tags having different levels of specificity. Some of the tags are verified with the digital content based on feature detection, and a correspondence to the digital content is determined for each verified tag. The correspondence of the verified tags is then propagated up the hierarchy to others of the tags that are semantically related to the verified tags but which were not verified by the feature detection. The verified tags and the verified other tags are then assigned to the digital content to control how the digital content is retrieved when subject to a search.

Auditing and augmenting user-generated tags for digital content

The present disclosure is directed toward systems and method for generating geo-fences having boundaries around geographic regions of interest based on historical activity data of a user application on a plurality of client devices. For example, systems and methods described herein involve tracking activity data to identify instances of an application activity associated with an action to promote and identify locations where users more frequently perform the application activity. In addition, systems and methods described herein involve tracking activity data over time to determine geographic regions of interest at different time periods where users more frequently perform application activities. Further, systems and methods described herein involve generating geo-fences around one or more geographic regions of interest.

Generating data-driven geo-fences

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Bengaluru, India

Payal Bajaj