Microsoft Technology Licensing, LLC

Nicolas A Champagne-Williamson

Aaron Michael Butcher

Anatolie Gavriliuc

Seung-Yup Chai

Ken M Sadahiro

Jerin R Schneider

Nandini Bhattacharya

Kangsan Lee

Philippe Favre

Kofi S Opoku

John D Malatras

Methods, systems, and computer program products are described herein for unsupervised clustering of browser history using web navigational activities. For example, correlation scores are calculated that indicate correlations between web pages indicated in a browsing history of a user. Moreover, the correlation scores are calculated based on web navigational activities determined from the browsing history. In addition, the web pages are clustered into a plurality of clusters based on the correlation scores and the clusters are ranked for relevancy to the user based on a relevancy algorithm. The relevancy algorithm determines a likelihood that a user will access a corresponding web page for each web page of a cluster. A cluster having a greatest ranking is identified and an indication of a web page of the identified cluster as a suggested web page to revisit is provided to a user.

Unsupervised clustering of browser history using web navigational activities

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Kirkland, WA, United States of America

Nicolas A Champagne-Williamson