Promoting Music Exploration through Personalized Nudging in a Genre Exploration Recommender

Abstract

Recommender systems are efficient at predicting users’ current preferences, but how users’ preferences develop over time is still under-explored. In this work, we study the development of users’ musical preferences. Exploring musical preference consistency between short-term and long-term preferences in data from earlier studies, we find that users with higher musical expertise have more consistent preferences for their top-listened artists and tags than those with lower musical expertise, while high expertise users also show more diverse listening behavior. Users typically chose to explore genres that were close to their current preferences, and this effect was stronger for expert users. Based on these findings, we conducted a user study on genre exploration to investigate (1) whether it is possible to nudge users to explore more distant genres, (2) how users’ exploration behavior within a genre is influenced by default recommendation settings that balance personalization with genre representativeness in different ways, and (3) how nudging for exploration increases the perceived helpfulness of recommendations for users to explore. Our results show that users were more likely to select the more distant genres if these genres were nudged by presented at the top of the list, however, users with high musical expertise were less likely to do so. We also find that the more representative default slider, which by default recommended more genre-representative tracks, made users set the slider at a less personalized level. The more representative slider position alone did not promote more exploration of users’ current preferences but when combined with the nudge for distant genres, the more representative slider positions effectively nudged users to explore. Nudging to explore does not necessarily lead to an increase in perceived helpfulness. On the one hand, nudging for exploration improves the perceived helpfulness for exploration by making users explore away from their current preferences. On the other hand, it reduces helpfulness due to lower perceived personalization as users move away from their personalized recommendations. To improve perceived helpfulness, it seems necessary to provide a balanced trade-off between exploration and personalization.