Microsoft Technology Licensing, LLC

Mohit Wadhwa

Nagaraj Kota

Venkatesh Duppada

Nadeem Anjum

Sumit Srivastava

Ashvini Kumar Jindal

Ramanujam Madhavan

Computer-implemented systems and methods for generating and using a location sensitive ensemble classifier for classifying content includes dividing a validation data set into regions. Each region encompasses data points of the validation data set that fall within the region. A regional ensemble classifier is generated for each region based on the data points that fall within the region. A content item is then classified in at least one of a plurality of classes using the regional ensemble classifier for the region to which the content item belongs.

Location sensitive ensemble classifier

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

Techniques for selecting models using greedy search on validation metrics are disclosed herein. A system generates corresponding predictions for a validation dataset using a plurality of prediction models. The system selects one of the prediction models for inclusion in an ensemble set based on the selected prediction model generating more correct predictions for the validation dataset than the other prediction models, and then removes the selected prediction model from the plurality of prediction models to form a reduced plurality of prediction models. The system identifies remaining data records of the validation dataset for which the selected prediction model generated an incorrect prediction, and then selects an additional prediction model from the reduced plurality of prediction models for inclusion in the ensemble set based on a determination that the additional prediction model generated more correct predictions for the remaining data records than the other prediction models in the reduced plurality of prediction models.

Model selection using greedy search

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.

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New Delhi, India

Mohit Wadhwa