Core Viewpoint - The article discusses the emergence of generative recommendation models, particularly the HSTU framework, which has shown significant advancements in the recommendation system landscape, especially with the successful deployment on domestic Ascend NPU [1][4][5]. Group 1: Development of Generative Recommendation Models - The generative recommendation paradigm, characterized by the expansion law, is becoming a future trend in recommendation systems [4][6]. - The evolution of recommendation systems has shifted from manual feature engineering to complex model designs, and now back to focusing on feature engineering due to the limitations of deep learning capabilities [5][6]. - The success of large language models has inspired researchers in the recommendation field to explore scalable models that can enhance recommendation effectiveness [5][6]. Group 2: Performance Analysis of Different Architectures - A comparative analysis of HSTU, Llama, GPT, and SASRec revealed that HSTU and Llama significantly outperform others in scalability as model parameters increase, while GPT and SASRec show limited scalability in recommendation tasks [7][9]. - HSTU consistently outperformed baseline models like SASRec in multi-domain scenarios, demonstrating its potential in addressing cold start problems [13]. Group 3: Key Components and Their Impact - The removal of the Relative Attention Bias (RAB) from HSTU led to a noticeable decline in performance, indicating its critical role in the model's scalability [9][11]. - Modifications to the residual connection and the introduction of RAB to SASRec improved its scalability, highlighting the importance of these components in enhancing traditional recommendation models [11][12]. Group 4: Future Directions - The report identifies potential research directions for generative recommendation models, including data engineering, tokenizer efficiency, and training inference efficiency, which could help address current challenges and expand application scenarios [18].