A Characterization of Generative Recommendation Models: Study of Hierarchical Sequential Transduction Unit

Recommendation systems are crucial for personalizing user experiences on online platforms. While Deep Learning Recommendation Models (DLRMs) have been the state-of-the-art for nearly a decade, their scalability is limited, as model quality scales poorly with compute. Recently, there have been research efforts applying Transformer architecture to recommendation systems, and Hierarchical Sequential Transaction Unit (HSTU), an encoder architecture, has been proposed to address scalability challenges. Although HSTU-based generative recommenders show significant potential, they have received little attention from computer architects. In this paper, we analyze the inference process of HSTU-based generative recommenders and perform an in-depth characterization of the model. Our findings indicate the attention mechanism is a major performance bottleneck. We further discuss promising research directions and optimization strategies that can potentially enhance the efficiency of HSTU models.