PsychFM: Predicting your next gamble
This work addresses the need for personalized prediction in gambling behavior, which is incremental as it builds on existing models by incorporating psychological factors.
The authors tackled the problem of predicting individual gambling choices by proposing PsychFM, a hybrid model combining machine learning and psychological theories, which outperformed random forest and factorization machines on the CPC-18 benchmark dataset.
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.