Abstract: Mobile device advertising chains are described herein. Presenting a primary offer, comprising a first action, is caused on a mobile computing device. The first action is caused on the mobile computing device. An end of the first action is detected. Presenting a purchase offer, comprising a purchase action, is caused on the mobile computing device after the end of the first action. Data indicating that the purchase action was completed in connection with the mobile computing device based on the purchase offer is received. A sum of the value to confer in connection with the device for accepting both the primary offer and the purchase offer is determined. The sum of the values may be used in optimizing subsequent presentations of the primary offer to other mobile devices.

Abstract: Mobile device advertising chains are described herein. Presenting a primary offer, comprising a first action, is caused on a mobile computing device. The first action is caused on the mobile computing device. An end of the first action is detected. Presenting a purchase offer, comprising a purchase action, is caused on the mobile computing device after the end of the first action. Data indicating that the purchase action was completed in connection with the mobile computing device based on the purchase offer is received. A sum of the value to confer in connection with the device for accepting both the primary offer and the purchase offer is determined. The sum of the values may be used in optimizing subsequent presentations of the primary offer to other mobile devices.

Abstract: The likelihood that a query belongs to a vertical is determined. The query is assigned to a vertical based on that likelihood. A query submitted to a main search box is assigned to verticals and processed using indices specific to those verticals. The query is assigned to verticals based on coverage adjusted log likelihood per unit (CALL) values for that query in those verticals. An offline learning component computes UNITS dictionaries and learns the distributions of query units in the main search and each of the vertical searches using query logs. An online scoring and ranking component uses the query distributions and UNITS dictionaries to determine the CALL values and a likelihood of the query belonging to any vertical. The search query is then assigned to verticals based on the likelihoods. The search query is then processed by all the verticals the query is assigned to.

Abstract: The likelihood that a query belongs to a vertical is determined. The query is assigned to a vertical based on that likelihood. A query submitted to a main search box is assigned to verticals and processed using indices specific to those verticals. The query is assigned to verticals based on coverage adjusted log likelihood per unit (CALL) values for that query in those verticals. An offline learning component computes UNITS dictionaries and learns the distributions of query units in the main search and each of the vertical searches using query logs. An online scoring and ranking component uses the query distributions and UNITS dictionaries to determine the CALL values and a likelihood of the query belonging to any vertical. The search query is then assigned to verticals based on the likelihoods. The search query is then processed by all the verticals the query is assigned to.

Abstract: A method and apparatus for geographic specific search results in response to a search request having an address field includes retrieving map portion having the address thereon. The method and system further includes determining entities in geographic relation to the address and generating an entity listing, where the entity listing includes listing of the various entities. These entities may represent any type of business, club, library, government building/office, or other locations. The method and apparatus further includes determining various descriptive terms that relate to the entities which are in geographic relation to the address. These terms include hyperlinks to corresponding entity listings. The method and apparatus further includes generating a visual display that includes: the map portion with the address displayed thereon; an entity portion that includes the entity listing; and a term portion with a taxonomical display of the plurality of terms.

Abstract: The present invention is directed towards systems and methods for generating recommendations in response to one or more users based on user search queries. The method of the present invention comprises generating a recommendation model based on aggregate activity generated though use of a network resource. A user profile is generated based on an individual user's interaction with said network resource. A user query is received and the previously generated recommendation model in combination with the previously generated user profile are utilized to provide a recommendation relevant to the user search query and global statistics.