Microsoft Technology Licensing, LLC

Huan Hoang

Krishnaram Kenthapadi

Yan Liu

Yongzheng Zhang

Shen Huang

Chi-Yi Kuan

Zachary Mason Roth

Yue Li

Guan Wang

Vibhu Prakash Saxena

Qingbo Hu

Quan Wang

Burcu Baran

Chia Lung Kao

Michael Charles Zinzan Fairley

A method and system for determining links for a linking page are provided. For each linking page of a plurality of linking pages, a similarity score between each linking page and each linked-to page is generated. Each linked-to page is associated with each linking page. Based on the similarity scores generated for each linking page, a set of linked-to pages for each linking page is identified. An importance score for the linking page is also generated. Identities of the plurality of linking pages, identities of the set of linked-to pages identified for each linking page, and the importance score for each linking page are stored as input. Based on the input, for each linking page, a subset of the set of linked-to pages identified for each linking page is assigned by a model.

Optimization of links to electronic content

Methods, systems, and computer programs are presented for selecting features for a machine-learning model configured to detect anomalies in the evolution of data over time. One method includes an operation for identifying one or more key fields and value fields from the fields in a relational database. The method also includes grouping data of the value fields based on values of the one or more key fields and calculating one or more statistical values for each group of data of the value fields. The method further includes operations for monitoring an evolution of the one or more statistical values over time, and for selecting, based on the evolution of the one or more statistical values over time, features to be used by a machine-learning model to detect anomalies in content of the relational database over time. The method also includes executing the machine-learning model to detect the anomalies.

Automatic feature generation for machine learning in data-anomaly detection

A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Key Feature Engine receives a request for a desired number of key features to be identified from a data set in at least a portion of a database. The Key Feature Engine executes instances of multiple types of machine learning data models on the data set to calculate respective regression coefficients, in each machine learning data model instance, for each feature in a plurality of features defined by a plurality of data categories of a social network service. The Key Feature Engine identifies at least one key feature, of one or more instances of the multiple types of machine learning data models, based on a value of a corresponding regression coefficient.

Understanding business insights and deep-dive using artificial intelligence

Techniques for automatically linking pages in a web site are provided. In one technique, training data for a machine-learned scoring model is generated that comprises a plurality of features related to content items. The training data comprises multiple entries, each corresponding to a different content item in a first set of content items. For each entry, a corresponding label is based on a ranking of the corresponding content item in one or more search engine results. The machine-learned scoring model is trained based on the training data. For each content item in a second set of content items, multiple attribute values associated with that content item are input into the machine-learned scoring model, which generates a result. Based on multiple results, determining, for a particular web page, a strict subset of the second set of content items to which the particular web page will include one or more links.

Automatically linking pages in a website

The disclosed embodiments provide a system for performing A/B testing for search engine optimization (SEO). During operation, the system divides a set of web pages into a control group, an inbound treatment group, an outbound treatment group, and a full treatment group. Next, the system generates a first set of links from the outbound treatment group to the inbound treatment group and a second set of links within the full treatment group. The system then applies an A/B test to pairs of groups selected from the control group, the inbound treatment group, the outbound treatment group, and the full treatment group. Finally, the system outputs a result of the A/B test for use in assessing an effect of the first and second sets of links on search engine results associated with the set of web pages.

A/B testing for search engine optimization

A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize keywords (potential search terms) based on their respective predicted contribution to the ranking of JSERPs. The value of a job-related keyword is expressed as a priority score assigned to that keyword. The SEO system generates priority scores for different keywords, using a probabilistic model that takes into account a value expressing how likely the keyword is to be included in a search query as a search term and/or a value expressing how likely is a search that includes the keyword as a search term is to produce relevant results.

Generating relevance scores for keywords

Generating popularity scores for keywords

Prioritizing keywords

An on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to partition a number of available links from authoritative web pages to Job Search Results Rages (JSERPs) in a way that maximizes gain expressed by a predetermined metric, such as, a metric representing a number of certain type of events observed over a period of time or an improvement in respective ranks generated for the JSERPs by a third party search engine.

Partitioning links to JSERPs amongst keywords in a manner that maximizes combined weighted gain in a metric associated with events of certain type observed in the on-line social network system with respect to JSERPs represented by keywords

A system, method, and apparatus are provided for depicting attributes of a user's or member's connections within a professional or social network, in a manner that enables insight into the nature of the connections. When insight into a first member's connections is to be depicted, one or more attributes of first-degree connections (i.e., direct connections) and/or other connections are retrieved (e.g., employer or associated organization, function or role, seniority, skill(s)), over all time or some threshold period of time into the past. The values for the attributes are aggregated over the examined connections, and some or all values are displayed with one or more visual characteristics (e.g., size, color) that indicate a frequency or commonness of the values among the connections. The system or apparatus may include a user interface through which the depiction is presented in place of or in addition to a display of the connections.

Depicting attributes of connections in a social network

An on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to partition a number of available links from authoritative web pages to Job Search Results Rages (JSERPs) in a way that maximizes combined improvement in respective ranks generated by a third party search engine for JSERPs represented by keywords from the set of keywords.

Partitioning links to JSERPs amongst keywords in a manner that maximizes combined improvement in respective ranks of JSERPs represented by respective keywords

Growing community of inventors

San Jose, CA, United States of America

Huan Hoang