User Segmentation for Controlling Recommendation Diversity

The quality of recommendations is known to be affected by diversity and novelty in addition to accuracy. Recent work has focused on methods that increase diversity of recommendation lists. However, these methods assume the user preference for diversity is constant across all users. In this paper, we show that users’ propensity towards diversity varies greatly and argue that the diversity of recommendation lists should be consistent with the level of user interest in diverse recommendations. We introduce a user segmentation approach in order to personalize recommendation according to user preference for diversity. We show that recommendations generated using these segments match the diversity preferences of users in each segment. We also discuss the impact of this segmentation on the novelty of recommendations.