Systems and Methods for Targeted Advertising

Abstract

Methods of generating recommendations may include obtaining social network data from one or more network resources. Word relationships may be created between selected words in the social network data to produce relationship data. Advertisement or other asset recommendations may be generated for a target user by analyzing browse information of the target user to identify one or more words. Other words in the relationship data may be identified that are related to the words in the target user's browse information. One or more advertisements may be identified having at least one keyword that corresponds to the other words. At least a portion of these advertisements may be selected from a data repository to provide to the target user.

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         21 . A system for generating targeted advertisement recommendations, the system comprising:
 a data aggregation module configured to obtain a plurality of words from a social-network; and   a relationship mining module in communication with the data aggregation module, the relationship mining module configured to create word relationships between selected ones of the plurality of words to produce relationship data, each of the word relationships reflecting a degree of association between two or more of the selected words, wherein the degree of association is based at least in part on an amount of social momentum between said selected words; and   generating targeted advertising based at least in part on said relationship data.   
     
     
         22 . The system of  claim 21 , wherein the relationship mining module is further configured to reassess the degree of association of the word relationships over time. 
     
     
         23 . The system of  claim 21 , wherein the degree of association is further based at least in part on at least one of:
 a frequency of the selected words in the social network data;   a recency of a subset of the social-network data that comprises the two or more selected words, or   an authority factor of the subset of the social-network data; and   
     
     
         24 . The system of  claim 21  further comprising a recommender module in communication with the relationship module, the recommender module configured to access user data to identify one or more first words, identify the one or more first words in the relationship data, identify one or more second words in the relationship data that have one or more of said word relationships with the one or more first words, and identify one or more advertisements having at least one keyword that corresponds to the one or more second words. 
     
     
         25 . The system of  claim 24  wherein said recommender module selects an advertisement from the one or more advertisements based on the degree of association between said keyword and the one or more second words. 
     
     
         26 . The system of  claim 24 , wherein the recommender module is further configured to weight the one or more first words based at least in part on a frequency of which the one or more first words occur in the user data. 
     
     
         27 . The system of  claim 21 , wherein the relationship mining module is further configured to create word relationships by creating one or more graphs of word relationships. 
     
     
         28 . The system of  claim 24  wherein the user data includes at least one of user behavioral data and user tracking data. 
     
     
         29 . The system of  claim 28  wherein the user behavioral data includes at least one of data on the user's browse behavior, the user's asset selection behavior, and the user's purchase behavior. 
     
     
         30 . The system of  claim 29  wherein the user tracking data includes at least one of data regarding a browse history of the user, a purchase history of the user, demographic data on the user, and geographic data related to the user.