The Performance of Personality-based Recommender System for Fashion with Demographic Data-based Personality Prediction

Abstract

Currently, the common method to predict personality implicitly (Implicit Personality Elicitation) is Personality Elicitation from Text (PET). PET predicts personality implicitly based on statuses written on social media. The weakness of this method when applied to a recommender system is the requirement to have minimal one social media account. A user without such qualification cannot use such system. To overcome this shortcoming, a new method to predict personality implicitly based on demographic data is proposed. This proposal is based on findings by previous researchers stating that there is a correlation between demographic data and personality trait. To predict personality based on demographic data, a personality model (rule) is needed. This model correlates demographic data and personality. To apply this model to a recommender system, another model is needed, that is preference model which connects personality and preference. These two models are then applied to a personality-based recommender system for fashion. From performance evaluation, the precision of and user satisfaction to the recommendation is 60.19% and 87.50%, respectively. When compared to precision and user satisfaction of PET-based recommender system (which are 82% and 79%, respectively), the precision of demographic data-based recommender system is lower whereas the satisfaction is higher.

An Effective Decision-Making Support for Student Academic Path Selection using Machine Learning

“…The use of Artificial Intelligence (AI) in general and more particularly, Machine Learning, in almost all fields, allows nowadays, to predict from the available data, a number of interesting elements for decision-making [6] [7]. Thus, it is easy to understand the importance of using Machine Learning to improve the quality of academic performance [8][9].…”