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Beyond the top-N: algorithms that generate recommendations for self-actualization

Recommender systems traditionally provide users with recommendations that match their preferences, which creates a personalized user experience and increases users' satisfaction. However, recommendations from traditional systems may sometimes be considered too personalized, which isolates users from a diversity of perspectives, content, and experiences, and thus make them less likely to discover new things. To overcome this drawback, we argue that recommenders should more actively keep the user "in-the-loop" by providing alternative recommendation lists that go beyond the traditional Top-N list. Such Recommender Systems for Self-Actualization follow a more holistic human-centered personalization practice by supporting users in developing, exploring and understanding their unique tastes and preferences. In this paper, we discuss a series of algorithms that generate four new recommendation lists. These lists enable the recommender to gain a more holistic view of the user and also allow the user to learn more about themselves.