  There is a sudden surge to model human behavior due to its vast and diverse
applications which includes modeling public policies, economic behavior and
consumer behavior. Most of the human behavior itself can be modeled into a
choice prediction problem. Prospect theory is a theoretical model that tries to
explain the anomalies in choice prediction. These theories perform well in
terms of explaining the anomalies but they lack precision. Since the behavior
is person dependent, there is a need to build a model that predicts choices on
a per-person basis. Looking on at the average persons choice may not
necessarily throw light on a particular person's choice. Modeling the gambling
problem on a per person basis will help in recommendation systems and related
areas. A novel hybrid model namely psychological factorisation machine (
PsychFM ) has been proposed that involves concepts from machine learning as
well as psychological theories. It outperforms the popular existing models
namely random forest and factorisation machines for the benchmark dataset
CPC-18. Finally,the efficacy of the proposed hybrid model has been verified by
comparing with the existing models.
