Reproduce “A Study on Enhancing User Engagement by Employing Gamified Recommender Systems” on OpenResearch

About this paper

Ali Fallahi, Azam Bastanfard, Amineh Amini, Hadi Saboohi

This paper offers a systematic review of gamified recommender systems, analyzing how they enhance user engagement across various application domains. It synthesizes existing approaches, identifies common methodological limitations such as reliance on customized datasets, highlights "Healthcare and Fitness" as a dominant application area, and proposes future research directions for the field.

Key insights

• Gamification effectively addresses data sparsity and cold-start issues in recommender systems by incentivizing user interaction and data contribution.
• Existing research frequently uses customized datasets, often lacks comparative analysis against state-of-the-art methods, and commonly relies on subjective evaluation metrics.
• Hybrid recommender systems, particularly combining Collaborative Filtering with other techniques, are the most commonly employed, with 'Healthcare and Fitness' identified as the dominant application domain.

Summary by alphaXiv.