Addressing Personalized Diversity in Eyewear Recommendation: a Lenskart Case Study
Lalit Kishore Vyas and Ludovico Boratto
In Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, New York, USA, 2025
This study addresses the challenge of limited diversity in recommender systems on e-commerce category pages, which often leads to reduced user engagement and satisfaction. Recognizing the limitations of traditional Factorization Machines (FM) in generating diverse recommendations, we propose a personalized diversity approach that combines re-ranking strategies with FM, enhanced by Generalist-Specialist (GS) scores to tailor diversity to individual user preferences. The re-ranking strategies explored include Maximal Marginal Relevance (MMR) and Determinantal Point Processes (DPP). Our results show improved balance between relevance and personalized diversity in offline experiments. Additionally, we investigate an alternative approach to personalized diversity through a contextual bandit model (LinUCB), where diversity emerges by balancing exploration and exploitation in predicted preferences. This evaluation highlights LinUCB’s ability to anticipate diverse recommendations by simulating adaptive responses without relying on active user feedback, offering a contrast to traditional re-ranking methods.