Google Inc.

Gregory J Friedman

Khanh B Nguyen

Michael Alex Frumkin

Karthik Gopalratnam

Ralph U Gasser

Clarence Christopher Mysen

Jennifer Liu Low

Alice S Tull

Myles A Sussman

Edward A Baltz

Christopher M Roat

David A Sloan

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

A bid for a content slot from a content provider is received. The bid includes a revenue-sharing bid. It is determined that the bid is a winning bid and content is presented from the content provider in the content slot. Presentation details are logged that include a time when the content was presented. At a later time, an indication is received of an action, the indication including an indication that the action is responsive to the content having been presented previously. Based on the action, the content provider is charged an appropriate amount based on the revenue sharing bid.

Managing revenue sharing bids

In one aspect, methods include receiving one or more first bids that have associated fixed prices, receiving one or more second bids that have dynamic prices that are adjusted at a time of auction in consideration of other bids in the auction, conducting an auction including pricing the one or more first bids and the one or more second bids, the one or more first bids being priced at their respective fixed prices and the one or more second bids being priced at respective amounts that maintain each second bid in a position marginally above a next-highest bid in the auction, and serving content based on one or more winning bids from the auction.

Mixing first and second price bids in an auction

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

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Mountain View, CA, United States of America

Gregory J Friedman