Google Inc.

Anja Hauth

Junbin Teng

Boris Mazniker

Luuk Van Dijk

Steffen Gerd Meschkat

Pascal Tom Getreuer

Radu Jurca

Alexander Sobol

Matsvei Zhdanovich

David Lecomte

Chih-Wei Chen

Ivan Zauharodneu

Alessandra Mazzia

Anders Torp Brodersen

Andrew Stack

Techniques for estimating social content interactions are provided. An example method includes determining counts of one or more user interactions with one or more content items created by an author, each user interaction of the one or more user interactions having an associated time, classifying, based on respective associated times of the user interactions, the determined counts of the user interactions with the content items into predetermined time intervals spanning a first duration, computing an engagement model for the author based on the classified counts corresponding to the predetermined time intervals and a number of the content items created by the author and estimating, at a particular time for a second duration, a number of interactions with a particular content item created by the author based on the engagement model and a determined actual number of interactions with the particular content item.

Estimating social content interactions

Systems and methods for determining a user engagement level for a content item are provided. In some aspects, indicia of one or more user interactions with a content item are received. Each user interaction in the one or more user interactions has an associated time and an interaction type. A user engagement level for the content item is determined based on the one or more user interactions, the associated times, and the interaction types. The user engagement level for the content item is stored in association with the content item.

Determining a quality score for a content item

Systems and methods for determining a quality score or a user engagement level for a content item are provided. The quality score is based on a recentness score of the content item, an affinity score between two users and a popularity score of the content item. The user engagement level for the content item is based on user interactions with the content item, associated times of the user interactions, and interaction types of the user interactions. The user engagement level for the content item is stored in association with the content item.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying geographic features. In one aspect, a method includes receiving a query. Geographic features are identified, each geographic feature being associated with one or more names, each geographic feature being associated with at least one name that includes the query. A feature-query score is computed for each geographic feature, including: for each name of the geographic feature that includes the query, identifying a computed feature-name score, wherein the feature-name score is computed based on a count of a number of occurrences of the name in a query log, wherein each occurrence is attributed to the feature; and computing the feature-query score based on the identified feature-name scores. The geographic features are ranked according to the feature-query scores.

Identifying geographic features from query prefixes

This disclosure relates to systems and methods for determining source information for subscribe and unsubscribe events to channels, indicating referring sources from which subscribers initiated the events, and presenting the source information to content providers of the channels.

Implicit determination of referral sources for subscriptions to channels

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Zurich, Switzerland

Anja Hauth