CRII: CHS: Recommender Systems for Self-Actualization

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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 project investigates a new direction for recommender systems research with the main goal of supporting users in developing, exploring, and understanding their unique personal preferences. The project will develop a new type of recommender system: a Recommender System for Self-Actualization (RSSA), which will support decisions that have a resounding influence on our life (e.g., choosing an education or a job) where we do not want to just take the easiest option, but rather to develop ourselves into unique individuals with a strong sense of determination that we have chosen the right path.In contrast to most existing recommender systems research, this project carefully considers the psychology of consumer choice processes and develops new types of recommendations to support these processes in novel ways (the RSSA features). The PI's team will develop a new recommender system outfitted with the capability to display a traditional Top-N recommendation list, plus each new RSSA feature. The team will then test the benefit of each RSSA feature against traditional Top-N recommendations in an online user experiment. This benefit will be measured by participants' subjective evaluation of the system and their usage behavior logs.