METHOD FOR TARGETING ELECTRONIC ADVERTISING BY DATA ENCODING AND PREDICTION FOR SEQUENTIAL DATA MACHINE LEARNING MODELS

摘要

A method of encoding sequential data that allows encoding a subsequence of full sequences as a composite data symbol, wherein a subsequence is comprised of a maximum of one original data element, and a maximum of K original data elements. These composite data symbols, arranged sequentially, can then be used to train a machine learning model, and thus reduce complexity when a strict ordering within the context of the original data subsequences is not required, while still modeling synergies between the sequential data elements. Further, the method determines a set of related data elements to a composite symbol at the next time step, given the original subsequence. Given this set of related data symbols, prediction can be performed with the machine learning model, by picking the maximal likelihood path using the disclosed search tree algorithm intended for state space models, which probabilistically model a hidden state given a prior hidden state, and probability of observable data symbols, given a hidden state. In addition, a method of training such a machine learning model based on a real-world embodiment of advertising/marketing data is presented. After a machine learning model of this nature has been trained, it then can be used for prediction using the search tree algorithm.