Introduction to behavior based recommendation system

Conditional random fields (CRFs) are probabilistic models for segmenting and labeling sequence data. CRFs address limitations of previous models like hidden Markov models (HMMs) and maximum entropy Markov models (MEMMs). CRFs allow incorporation of arbitrary, overlapping features of the observation sequence and label dependencies. Parameters are estimated to maximize the conditional log-likelihood