Patent ID: 7698410
Filing Date: 2010-04-13
Classification: G06F,H04L

Abstract:
1. A computer-implemented method for automatically adjusting targeting of web users based upon user web traffic pattern data, the method comprising: executing computer program instructions for, at a first time, generating, using a processor, a first data model for web traffic pattern data using an incremental modeling process, wherein an incremental modeling process is a modeling process that can be re-trained in an amount of time that is linearly proportional with the size of a delta defined as newly added data plus expired data, wherein the generating includes generating a series of counts for combinations between individual users and particular web addresses, wherein the counts represent the probability that the individual user will navigate a web browser to the corresponding particular web address, and wherein the incremental modeling process bases the probabilities on the user web traffic pattern data over a fixed length of time, resulting in a count table containing the counts organized by total counts per user and web address combination over the entire fixed length of time and an incremental table containing total counts per user and web address combination over incremental periods of time smaller in length than the fixed length of time, wherein the incremental modeling process is a Naive Bayesian classifier modeling process and includes: executing computer program instructions for recommending, using the processor, a web page or content of interest to a user by using the first data model; executing computer program instructions for, at a second time later than the first time, generating, using the processor, a second data model for web traffic pattern data by retraining the first data model using the incremental modeling process, wherein the retraining includes basing new counts for combinations between individual users and particular web addresses on web traffic pattern data received between the first time and the second time and on web traffic pattern data in the incremental table, while ignoring web traffic pattern data in the incremental table representing the oldest web traffic pattern data in the incremental table over a length of time equal to the length of time between the first time and the second time; executing computer program instructions for updating, using the processor, the count table and incremental table based upon the retraining; deriving, using the processor, a model difference between the first data model and the second data model; and if the model difference is greater than a set threshold, recommending, using the processor, a web page or content of interest to a user by using the second data model.