Microsoft Technology Licensing, LLC

Song Lin

Songtao Guo

Yan Liu

Ke Wang

Alex Ching Lai

Aarti Kumar

Christopher Matthew Degiere

Kuo-Ning Huang

Boyu Zhang

Cindy Zhou

Yunjing Zhang

Keith Wai Kit Tsang

Rekha Thakur

Techniques are provided for using machine learning techniques to identify predictive features and predictive values for each feature. In one technique, a model is trained based on training data that comprises training instances, each of which corresponds to multiple usage-based features of an online service by a user. For each usage-based feature in a subset of the usage-based features, the model is used to generate a dependency graph, a histogram is generated, and an optimized value is selected based on the dependency graph and the histogram. A user of the online service is identified, along with a usage value that indicates a level of usage, by the user, of a usage-based feature. A comparison between the usage value and an optimized value of the usage-based feature is performed. Based on the comparison, it is determined whether to present data about that usage-based feature to the user.

Machine learning techniques to identify predictive features and predictive values for each feature

In an example embodiment, a member profile corresponding to a member of a social networking service is obtained. Usage information for the member is then obtained, and one or more member metrics are calculated based on the member profile and usage information for the corresponding member. A plurality of features are extracted from the member profile and the one or more member metrics. The plurality of features is inserted into an organization name confidence score model to obtain a confidence score for an organization name in the member profile.

Inferring attributes of organizations using member graph

Methods and systems for generating tailored user interface presentations based on skills clusters and automatically modified member profiles are presented. According to various embodiments, a set of skills are accessed and a skills matrix generated. A set of co-occurrences among the set of skills are identified. A set of skills clusters is automatically generated based on identifying of the co-occurrences and the skills clusters are automatically validated. A graphical representation of the validated skills cluster is presented with user interface elements for modifying the validated skills cluster and data representing member profiles is presented based on the validated skills cluster.

Generating graphical presentations using skills clustering

Growing community of inventors

Santa Clara, CA, United States of America

Song Lin