Exploring the Potential of Generative AI for Augmenting Choice-Based Preference Elicitation in Recommender Systems

The recent boost in generative artificial intelligence has also reached the field of recommender systems. However, as is often the case, much of the work focuses on the algorithms, overlooking the crucial aspect of improving the systems from a user perspective. In this initial research, we explore the potential of large language models to achieve improvements in preference elicitation. The interactive choice-based method we are augmenting has previously demonstrated significant improvements in a number of aspects related to the user experience. Through an exploratory user study, we show that the item set comparisons presented by this method can be successfully accompanied by independently generated textual summaries, thereby improving the user experience even further.