From 477d636969bcea077ba7c6b03fc5440275e5a5b3 Mon Sep 17 00:00:00 2001 From: davidediruscio Date: Mon, 9 Jun 2025 16:59:55 +0200 Subject: [PATCH] [logseq-plugin-git:commit] 2025-06-09T14:59:54.529Z --- pages/RecSys_2025_Reproducibility_711.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pages/RecSys_2025_Reproducibility_711.md b/pages/RecSys_2025_Reproducibility_711.md index 8bfd6cc0..20e65ad3 100644 --- a/pages/RecSys_2025_Reproducibility_711.md +++ b/pages/RecSys_2025_Reproducibility_711.md @@ -17,4 +17,4 @@ collapsed:: true - Overall presentation: Overall, the paper is well written and structured. Both the goal and the proposed solution are properly described. I have only a few concerns related to some missing justifications or explanations. In particular, I'm not convinced on why a mobile setup is considered and why such a setup has been used to compare the original environments with that presented in this work. - Technical Soundness: The [[Paper]] defines interesting research questions especially that concerning the inference phase. The methodological pipeline is clearly described in the online material, The insights of the first research questions, while valid, are obvious and expected to me (i[[.]]e., newer hardware being more efficient). It is necessary to stress what's the take away message and why it is [[IMPORTANT]], and especially what's can be built on top of it. - Reproducibility of methods: The online appendix contains all the artifacts that are needed to reproduce the experiments. I've played a bit with the docker image made available by the authors. Even though I could not execute the whole process until the end, I managed to easly start the pipelines by following the instructions made available by the authors. - - Impact: Carbon footprint in RS is indeed a relevant and interesting topic. While some results are insightful (e.g., training/inference trafe-offs) other are rather expected may not be impactfull for the community. However, the emphasis on model validation overhead adds useful considerations for future research direction related to \ No newline at end of file + - Impact: Carbon footprint in RS is indeed a relevant and interesting topic. While some results are insightful (e.g., training/inference trafe-offs) other are rather expected may not be impactfull for the community. However, the emphasis on model validation overhead adds useful considerations for future research direction related to sustainable RS. \ No newline at end of file