These results show that for MI weighting many important features appear further down in the ranked vectors, while for the bootstrapped weighting adding too many features adds mostly noise, since most characteristic features are concentrated at the top ranks.
in addition to better feature weighting, the bootstrapping step provides effective feature reduction, which improves vector quality and consequently the similarity results.