Google Inc.

Ralph U Gasser

Michael Alex Frumkin

Khanh B Nguyen

Karthik Gopalratnam

Gregory J Friedman

Myles A Sussman

Edward A Baltz

William T Neveitt

Advertisement quality measures (e.g., predicted click through rates) are modified in accordance with a user's preferences with respect to domains to which the advertisements relate.

User-based ad ranking

Methods and systems to modify the number and/or positioning of advertisements presented to a user based on that user's activity patterns (e.g., previous queries, query result selections, ad selections, etc.) and the order in which events constituting the activity patterns occurred. For example, the methods and systems can be based on a Markov model utilizing the user's activity patterns.

User-based advertisement positioning using markov models

System and method for quickly selecting content items (e.g., documents) having highest ranking scores in response to a query for content items. Base scores for a plurality of content items are determined based on attributes of the content items. One or more attributes are extracted from the query, the base scores of at least some of the content items are adjusted based on relationships between the query attributes and the content item attributes. A subset of the plurality of content items having the highest adjusted scores are selected, and the selected subset of content items are output.

Model based ad targeting

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Palo Alto, CA, United States of America

Ralph U Gasser