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There is a sudden surge to model human behavior due to its vast and diverseapplications which includes modeling public policies, economic behavior andconsumer behavior. Most of the human behavior itself can be modeled into achoice prediction problem. Prospect theory is a theoretical model that tries toexplain the anomalies in choice prediction. These theories perform well interms of explaining the anomalies but they lack precision. Since the behavioris person dependent, there is a need to build a model that predicts choices ona per-person basis. Looking on at the average persons choice may notnecessarily throw light on a particular person's choice. Modeling the gamblingproblem on a per person basis will help in recommendation systems and relatedareas. A novel hybrid model namely psychological factorisation machine (PsychFM ) has been proposed that involves concepts from machine learning aswell as psychological theories. It outperforms the popular existing modelsnamely random forest and factorisation machines for the benchmark datasetCPC-18. Finally,the efficacy of the proposed hybrid model has been verified bycomparing with the existing models.

PsychFM: Predicting your next gamble