Update README.md

README.md

@@ -73,10 +73,11 @@ Readers should not interpret summary statistics of this dataset as ground truth
 
 ## Challenges 
 
-The first challenge comes from handling the different types of data that are common in tables, the mixed-type columns: there are both numerical and categorical features that have to be embedded [Gorishniy et al., 2021, 2022, Grinsztajn et al., 2022, Shwartz-Ziv and Armon, 2022, Matteucci et al., 2023]. 
-In addition, some of the features have long-tail phenomenon and products have popularity bias. Our datasets contains more than 1,000,000 lines, while current high-performing models are under-explored in scale, e.g. the largest datasets in Grinsztajn et al. [2022] are only 50,000 lines, while in Gorishniy et al. [2021, 2022] only one dataset surpasses 1,000,000 lines. 
-Additional challenge comes from strongly imbalanced data: the positive class proportion in our data is less than 0.007 that leads to challenges in training robust and fair machine learning models [Jesus et al., 2022, Yang et al., 2024]. In our dataset there is no significant imbalances in demographic groups users regarding the protected attribute (both genders are sub-sampled with 0.5 proportion, female profile users were shown less job ad with 0.4 proportion and slightly less senior position jobs with 0.48 proportion), however, there could be a hidden effect of a selection bias. 
-This poses a problem in accurately assessing model performance [van Breugel et al., 2024]. 
+The first challenge comes from handling the different types of data that are common in tables, the mixed-type columns: there are both numerical and categorical features that have to be embedded. 
+In addition, some of the features have long-tail phenomenon and products have popularity bias. Our datasets contains more than 1,000,000 lines, while current high-performing models are under-explored in scale. 
+Additional challenge comes from strongly imbalanced data: the positive class proportion in our data is less than 0.007 that leads to challenges in training robust and fair machine learning models. 
+In our dataset there is no significant imbalances in demographic groups users regarding the protected attribute (both genders are sub-sampled with 0.5 proportion, female profile users were shown less job ad with 0.4 proportion and slightly less senior position jobs with 0.48 proportion), however, there could be a hidden effect of a selection bias. 
+This poses a problem in accurately assessing model performance. 
 More detailed statistics and exploratory analysis are referred to the supplemental material of the associated paper linked below.
 
 ## Metrics