# Weight Adjustment Framework for Self-Attention Sequential Recommendation
## 5 Conclusions  
Self-attention-based sequential recommendation models currently face challenges due to inaccurate attention weight distributions caused by noisy data inputs. We addressed this challenge with two approaches, building on the AC-TSR [15] model. AC-TSR reallocates attention weights based on the contribution of each historical item to the model's predictions to mitigate the issue of noisy inputs. Unlike AC-TSR, we added a denoising module and a logical representation layer. The denoising module enhances data quality, thereby improving the distribution of attention layer weights. Additionally, the logical representation layer models the uncertainty of user preferences and deepens the model's understanding of user behavior, thus refining the attention weight distribution during training. Ultimately, we validated the effectiveness of our proposed method through extensive experiments. We compared WAF-SR with several baseline methods, and, across multiple datasets and various evaluation metrics, WAF-SR consistently achieved the highest accuracy. We conducted ablation studies to individually remove the filtering and logical representation layers, demonstrating that these added components significantly impact recommendation performance. We also studied the hyperparameters within the model, observing the impact on WAF-SR by adjusting the values of these parameters. We provided settings for some of WAF-SR's parameters to enable researchers to replicate our experiments. In future production implementations, not only does the WAF-SR algorithm have the potential to  
Figure 3: The impact of different values of the hyperparameter \(\delta\). (**a**) The impact of different values of hyperparameter \(\delta\) on Recall@20. (**b**) The impact of different values of hyperparameter \(\delta\) on NDCG@20. Figure 2: The influence of the number of self-attention layers. (**a**) The influence of the number of self-attention layers on Recall@20. (**b**) The influence of the number of self-attention layers on NDCG@20.  
help enterprises uncover market trends and user needs, providing robust data support for product development and marketing strategy adjustments, but its superior recommendation performance can also significantly enhance user satisfaction and conversion rates. However, despite its considerable advantages, WAF-SR also poses certain limitations. Specifically, the introduction of the denoising module and logical representation layer may lead to model overfitting, especially in scenarios with small datasets or limited feature diversity. Given these challenges, we plan to further research and develop more efficient and robust denoising techniques in future work to reduce the likelihood of overfitting and enhance the model's generalizability across various application scenarios. [PERSON]-[PERSON] performed experiments, contributed to the design of the sequential recommendation model, performed detailed analyses, and wrote the manuscript. [PERSON] set the direction of the study and made final corrections. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original data presented in the study are openly available in Amazon Review Data at [[http://jmcauley.ucsd.edu/data/amazon/](http://jmcauley.ucsd.edu/data/amazon/)]([http://jmcauley.ucsd.edu/data/amazon/](http://jmcauley.ucsd.edu/data/amazon/)), accessed on 20 March 2024, and Yelp Open Dataset at [[https://www.yelp.com/dataset](https://www.yelp.com/dataset)]([https://www.yelp.com/dataset](https://www.yelp.com/dataset)), accessed on 20 March 2024. Our source code is available at [[https://github.com/AngSZ/WAF-SR/tree/main](https://github.com/AngSZ/WAF-SR/tree/main)]([https://github.com/AngSZ/WAF-SR/tree/main](https://github.com/AngSZ/WAF-SR/tree/main)), accessed on 20 March 2024.