Abstract: A pre-computed concept map represents concepts, concept metadata, and relationships between the plurality of concepts. Online user behavior may be predicted by correlating one or more online events of a user with one or more features of the concept map, aggregating a concept map history of the user to obtain online behavior over time, aggregating online behavior of the user and one or more other users to obtain aggregated online user behavior, and predicting future online behavior of the user based at least in part on the online behavior of the user and the aggregated online user behavior. The predicted behavior may be used to target ads that the user is likely to find relevant.

Abstract: Discovering relevant concepts and context for content nodes to determine a user's intent includes identifying one or more concept candidates in a content node based at least in part on one or more statistical measures, and matching concepts in a concept association map against text in the content node. The concept association map represents concepts, concept metadata, and relationships between the concepts. The one or more concept candidates are ranked to create a ranked one or more concept candidates based at least in part on a measure of relevance. The ranked one or more concept candidates is expanded according to one or more cost functions. The expanded set of concepts is stored in association with the content node.

Abstract: A method and apparatus for optimizing long-term revenues in online auctions of distinguishable units of an item. For example, the item might be a specific keyword in sponsored search where the distinguishable units could be different slots for putting ads. The system can estimate parameters such as relevance and value by using a notion of fairness and can optimize the revenue by effecting the users' incentives and by improving the bidding language. In general, the method and the system can also be used for equivalent offline auctions.

Abstract: Methods and apparatus for a new approach to the problem of matching relevant content to user queries. Instead of looking for the exact keyword, the invention expands it into groupings of concepts and phrases, where each such group represents one possible user intention (as implied by the query phrase or keyword). Each such grouping is analyzed to provide relevant content, including but not limited to, unstructured data like world wide web, categorized data and paid listings. The provided method can better capture user intentions even for cases where there is no click-through information.