Abstract: A method for detecting click fraud. In one embodiment, the method includes the steps of: directing a user to an item but delaying the internal ratings update of the item; collecting data to build a model of the user; building the model of the user in response to the collected data; determining the trustworthiness of the user in response to the model of the user; and updating internal ratings of the item in response to the trustworthiness of the user. In various embodiments, the click fraud can be the inflation of advertisement clicks, the inflation of popularity or the inflation of reputation.

Abstract: A method for making an online content recommendation. In one embodiment, the method includes the steps of: providing a plurality of data source modules having data; providing a plurality of function modules, each function module adapted to be connected to at least one of the plurality of data source modules and other function modules; receiving a recommendation request; dynamically connecting at least one of the plurality of the data source modules and at least one of the plurality of function modules in response to the recommendation request; and generating the recommendation by using the connected at least one data source module and the at least one function module.

Abstract: A method for detecting click fraud. In one embodiment, the method includes the steps of: directing a user to an item but delaying the internal ratings update of the item; collecting data to build a model of the user; building the model of the user in response to the collected data; determining the trustworthiness of the user in response to the model of the user; and updating internal ratings of the item in response to the trustworthiness of the user. In various embodiments, the click fraud can be the inflation of advertisement clicks, the inflation of popularity or the inflation of reputation.

Abstract: A method for providing personalized advertisement to a first user. The method includes the steps of: displaying a first advertisement to users; defining segments for a user community preference; assigning each of the users to one of the segments in response to the user's level of interest in the user community preference; identifying a first group of the users interested in the first advertisement; identifying a second group of the users not interested in the first advertisement; determining the percentage of the first group of users assigned to the segment; determining the percentage of the second group of users assigned to the segment; determining whether each segment is an indicator for user interest in the first advertisement by comparing the two percentages associated with the segment; and targeting a second advertisement to users assigned to the segment if the segment is an indicator for user interest in the first advertisement.