US 11,443,005 B2Granted

Unsupervised clustering of browser history using web navigational activities

AssigneeMicrosoft Technology Licensing, LLC

InventorsKen M. Sadahiro, Aaron M. Butcher, Philippe Favre, Anatolie Gavriliuc, Kofi S. Opoku, Seung-Yup Chai, Nandini Arijit Bhattacharya, John D. Malatras, Nicolas A. Champagne-Williamson, Kangsan Lee, Jerin R. Schneider

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Abstract

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.

Claims20

Dependent claims

A computing device, comprising:

one or more processors;
and
one or more memory devices that store executable computer program logic for execution by the one or more processors, the executable computer program logic comprising:
a correlation score determiner configured to calculate correlation scores that indicate correlations between web pages indicated in a browsing history of a user, the correlation score determiner configured to calculate correlation scores for pairs of web pages in the browsing history, each correlation score of the correlation scores calculated based on a combination of coefficients corresponding to types of web navigational activities;
a cluster generator configured to cluster the web pages into a plurality of clusters based on the correlation scores;
a cluster relevancy evaluator configured to rank the clusters for relevancy to the user based on a relevancy algorithm, the relevancy algorithm configured to determine a plurality of likelihoods, each likelihood of the likelihoods being a likelihood of the user accessing a corresponding web page of the web pages;
and
a cluster selector configured to
identify a cluster of the clusters having a greatest ranking, and
provide, to the user, an indication of a web page of the identified cluster as a suggested web page to revisit.

7 more elements

A computer-readable storage medium having program instructions recorded thereon that, when executed by at least one processor of a client computing device, causes the at least one processor to perform a method comprising:

calculating correlation scores that indicate correlations between web pages indicated in a browsing history of a user, wherein said calculating comprises calculating correlation scores for pairs of web pages in the browsing history, each correlation score of the correlation scores calculated based on a combination of coefficients corresponding to types of web navigational activities;
clustering the web pages into a plurality of clusters based on the correlation scores;
ranking the clusters for relevancy to the user based on a relevancy algorithm, the relevancy algorithm configured to determine a plurality of likelihoods, each likelihood of the likelihoods being a likelihood of the user accessing a corresponding web page of the web pages;
identifying a cluster of the clusters having a greatest ranking;
and
providing, to the user, an indication of a web page of the identified cluster as a suggested web page to revisit.

3 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/428,755Filed 31 May 2019
Published: US 2020/0380051 A13 Dec 2020
Granted: 13 Sep 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification8 codes

G06F 16/954
Retrieval from the web: navigation, e.g. using categorised browsing
G06F 16/906
Details of database functions independent of the retrieved data types: clustering; Classification
G06F 16/951
Retrieval from the web: indexing; Web crawling techniques
G06F 16/9535
Querying, e.g. by the use of web search engines: search customisation based on user profiles and personalisation

Family

US 2020/0380051 A1Pre-grant publication · published 3 Dec 2020
US 11,443,005 B2This patent

From the US filing history. Foreign family members aren’t listed.

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