I assume you are referring to our white paper, and to the recommendations for priors settings based on number of attributes and sample size. I also assume you are using our CBC/HB Model Explorer tool to automate the many HB runs and jack-knife sampling of holdout tasks.
There is quite a bit of variability in Degrees of Freedom...even in prior variance across data sets, so the recommendations in the white paper are generalities. Each CBC study has specific characteristics due to amount of heterogeneity in the preferences across people and respondent engagement/error.
The different between degrees of freedom 5 and degrees of freedom 20 are not very big in my experience.
A big question is if the hit rates changed very much between the default settings (D.F.=5, Prior Var=1) and what turned out to be the "optimal" setting according to the jack-knife search.
That said, based on our experience, if you have a lot of attributes in full-profile in CBC, prior variance of 0.2 can work better in general for prediction quality than prior variance of 1.0.