Microsoft Technology Licensing, LLC

Venkatesh Duppada

Nagaraj Kota

Mohit Wadhwa

Nadeem Anjum

Ashvini Kumar Jindal

In an example embodiment an approximate nearest neighbor framework is provided to query user activity data to find users who are similar to users who have been 'matched' to a particular piece of content but who otherwise would not have been matched on their own. The users who have been matched may be called a seed set of users, which are known in real-time, or near-real-time. Use of the approximate nearest neighbor framework allows the system to expand instantly the initial seed set of users to other similar users to rapidly distribute relevant pieces of content to active users, increasing liquidity of the system. Additionally, the target set of specific users to which a notification is sent about the pieces of content can also be expanded, increasing the recall rate.

Instant content notification with user similarity

In an example embodiment, user interactions with a graphical user interface are modeled to derive an efficient representation that is highly available through a framework. This representation enables downstream analysis as to the relevancy of the user interactions through libraries leveraging standardized activity representations. With these components, it becomes possible to derive user intent in a modular fashion, domain by domain, while decoupling many system aspects, and also providing high capacity and precise intent information to leverage for personalization.

Unified intent understanding for deep personalization

The disclosed embodiments provide a system for processing data. During operation, the system obtains a first sentence representing a first sequence of actions between a user and a set of jobs. Next, the system applies a language model to token embeddings of a first set of tokens in the first sentence and position embeddings of token positions in the first sentence to produce a first set of output embeddings. The system then combines the first set of output embeddings into a first session embedding that encodes the first sequence of actions. Finally, the system outputs the first session embedding for use in characterizing job-seeking activity of the user.

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Bengaluru, India

Venkatesh Duppada