You Today, Better Tomorrow: Envisioning the Role of Conversation in Recommender Systems of the Future

Manveer Kalirai, Anastasia Kuzminykh

Abstract: Recommender systems could evolve from traditional models of recommendation that largely harness data on past interactions to predict what a user might want in a given moment, towards systems that also support and nurture user self-actualization. This shift could guide users in exploring and fulfilling the needs of their future potential selves, untethered from their past and current identities. In this provocation, we suggest that interactive conversational recommendation is a suitable means to rouse this vision. Conversational recommendation is capable of eliciting real-time and layered preferences, and can enable systems to take on a more proactive role in dialoguing with users about their aspirational needs—particularly in helping users navigate the intricacies that often surround these needs. We also examine the potential challenges associated with the realization of such recommender systems—for instance, the complexities in transitioning from past-based patterns of personalization to those that accommodate present-oriented and future-oriented personalization, and the preservation of user agency whilst broadening the scope of roles recommender systems can play. Overall, this paper advocates for a necessary progression in recommender systems, one propelled by conversational recommendation, towards designs that not only avail present-day user needs, but also actively stimulate pathways toward the actualization of their potential and aspirational future selves.