and questions:
There lacks an introduction of Laplacian matrix but directly uses it. A paper should be self-contained.
The motivation is not strong. The authors stated that "... the transformer architecture ... outperformed many SOTA models ... motivates us". This sounds like "A tool is powerful, then I try the tool on my task, and it works well! Then I publish a paper". However, it lacks of analysis why Transformer works well on this task, which would bring more insights to the community.
Section 3.3 needs to be polished more on writing.
1 In Eqn. (8), The e^{(t)} is proposed without further explanation. What is it? Why is it needed? What is the motivation of proposing it?
2 In Eqn. (8), are f_\theta(v_j) and \hat{y}_j^{(t)} the same thing since they both are the predictions of v_j by the predictor.
3 In line 177-178, if the y_j^{true} is "user-unkonwn", then how do you compute e^{(t)} in Enq. (8)?
4 Why this SE framework can help to improve, how does it help? Similar to 2, please DO NOT just show me what you have done and achieved, but also show me why and how you manage to do these.
I would consider increasing the rating based on the authors' response.
Reference: [1] Luo, et al. "Neural architecture search with gbdt." arXiv preprint arXiv:2007.04785 (2020). https://arxiv.org/abs/2007.04785
3 In line 177-178, if the y_j^{true} is "user-unkonwn", then how do you compute e^{(t)} in Enq. (8)?