Multiobjective Clustering-Guided Multitasking for Diversified Sequential Recommendation

Sequential recommendation (SR) aims to predict users’ next-item preferences by analyzing their historical interaction sequences. Most existing SR models primarily focus on user preferences for a single objective of item relevance; they often overlook users’ personalized preferences for diversified item attributes. Although recent studies have attempted to address this limitation, efficiently resolving the inherent accuracy-diversity trade-off remains an open challenge. Drawing inspirations from multiobjective optimization and evolutionary multitasking, this paper proposes a multiobjective clustering-guided multitasking framework (MCGM-SR) that reconciles the two competing objectives in sequential recommendation. To enable knowledge transfer across homologous preference patterns, a user-level multiobjective clustering mechanism is introduced to group users with similar behavioral trajectories into objective-oriented clusters. Within each cluster, a multiobjective multitasking algorithm facilitates synergistic optimization across tasks to improve convergence efficiency. Extensive experiments on two real-world public datasets demonstrate that MCGM-SR achieves significant improvements in both accuracy and diversity metrics, compared to state-of-the-art sequential models and their diversified baselines. Further ablation studies reveal that the proposed clustering method significantly enhances optimization performance by effectively facilitating knowledge transfer.