Hierarchical hidden markov model-based recommendation system and method with combined taxonomy and intent state representation

Abstract

Certain aspects of the disclosure provide methods, systems, and apparatuses for providing taxonomy-boosted hierarchical hidden Markov modeling to enhance recommendation inferences and machine learning model training. A hierarchical structure of enterprise knowledge is constructed to provide searching knowledge in a desired granularity. A hidden state space may be defined for a hierarchical hidden Markov model, where each hidden state in the hidden state space is associated with a node in the hierarchical structure and an intent category obtained from observing the actions, events, and behavior of the user. Training a classification model may be provided to predict a probability of each hidden state in the hidden state space based on a user input. Training the hierarchical hidden Markov model may be provided based on a training data set labeled for each hidden state in a hidden state space.