Why type 8 quantile function is the best? https://www.researchgate.net/publication/222105754_Sample_Quantiles_in_Statistical_Packages
We believe there is a similar need
to adopt a standard sample quantile definition, and we propose 
that $\hat{Q}_8(p)$ is the best choice.

The above paper says this. It is not clear based on what it is the best. Could anybody help explain?
Since this is an old paper. Is this recommendation still the best according to the current knowledge?

R lists several types of slightly different definitions of sample quantiles. Each is claimed to be "better" for some specific applications. Textbook "definitions," sometimes giving intervals for "a quantile," rather than specific points for "the quantile," are often not
sufficiently specific to distinguish among "types."
Quantiles are often used for very large samples, and for them the differences among types are often relatively small and can usually be ignored.
It is untidy that there is no consensus as to "the best" definition. Your linked article is not the first complaint about the untidiness, nor I suspect, the last.
Example: In R type=7 is default:
set.seed(1234); x = rnorm(200, 100, 15)  
quantile(x,.75); quantile(x,.75, type=8)
     75% 
108.2989   # default type 7 
     75% 
108.3642   # link's type 8

sort(x)[149:151]
[1] 107.7064 108.2500 108.4458

For comparison, quantile 0.75 of $\mathsf{Norm}(\mu=100,\sigma=15)$ is $110.1123.$
qnorm(.75, 100, 15)
[1] 110.1173

