Recommender Systems for Self-Actualization

Every day, we are confronted with an abundance of decisions that require us to choose from a seemingly endless number of choice options. Recommender systems are supposed to help us deal with this formidable task, but some scholars claim that these systems instead put us inside a "Filter Bubble" that severely limits our perspectives. This paper presents a new direction for recommender systems research with the main goal of supporting users in developing, exploring, and understanding their unique personal preferences.