Idea of a new Personality-Type based Recommendation Engine

TL;DR

This paper proposes a novel recommendation engine that uses Myers-Briggs personality types to personalize suggestions for books, music, movies, and games, based on subjective preference data collected through a survey.

Contribution

It introduces a new personality-based approach for content recommendation, leveraging MBTI types and a custom survey to enhance personalization beyond traditional methods.

Findings

Survey collected over 100 features related to personality preferences.

Analysis revealed correlations between MBTI types and content preferences.

Framework for integrating personality data into recommendation systems.

Abstract

Myers-Briggs Type Indicator (MBTI) types depict the psychological preferences by which a person perceives the world and make decisions. There are 4 principal functions through which the people see the world: sensation, intuition, feeling, and thinking. These functions along with the Introverted\Extroverted nature of the person, there are 16 personalities types, the humans are divided into. Here an idea is presented where a user can get recommendations for books, web media content, music and movies on the basis of the users' MBTI type. Only things like books and other media content has been chosen because the preferences in such things are mostly subjective. Apart from the recommended content that is generally generated on the basis of the previous purchases, searches can be enhanced by using the MBTI. A minimalist survey was designed for collecting the data. This has a more than 100 features that show the preference of a personality type. Those include preferences in book genres, music genres, movie genres and even video games genres. After analyzing the data that is collected from the survey, some inferences were drawn from it which can be used to design a new recommendation engine for recommending the content that coincides with the personality of the user.