Weakness: 1. The novelty of this paper is limited. First, the analysis of the vertex-level imbalance problem is not new, which is a reformulation of the observations in previous works [Rendle and Freudenthaler, 2014; Ding et al., 2019]. Second, the designed negative sampler uses reject sampling to increase the chance of popular items, which is similar to the proposed one in PRIS [Lian et al., 2020]. 2. The paper overclaims on its ability of debiasing sampling. The “debiased” term in the paper title is confusing. 3. The methodology detail is unclear in Sec. 4.2. The proposed design that improves sampling efficiency seems interesting but the corresponding description is hard to follow given the limited space. 4. Space complexity of the proposed VINS should also be analyzed and compared in empirical studies, given that each (u, i) corresponds to a b u f f e r u i
. 5. Experiment results are not convincing enough to demonstrate the superiority of VINS on effectiveness and efficiency. - For effectiveness, the performance comparison in Table 1 is unfair. VINS sets different sample weights W u i
in the training process, while most compared baselines like DNS, AOBPR, SA, PRIS set all sample weights as 1. - For efficiency, Table 2 should also include the theoretical analysis for contrast.
- For effectiveness, the performance comparison in Table 1 is unfair. VINS sets different sample weights W u i in the training process, while most compared baselines like DNS, AOBPR, SA, PRIS set all sample weights as 1.