How to compute K and n? [Item-based Collaborative Filtering]
My guess is that these are free parameters of the algorithm that you must set somehow.  For instance, you might evaluate different choices of these parameters on a set of training data to see which performs the best.  You could also use cross-validation or other techniques to evaluate different settings of these parameters.

Note that p.46 of the thesis says "This threshold value is based on the predicted ratings that are achieved after running the item-based collaborative filtering algorithm."  That sounds consistent with my interpretation above.

In general, I would recommend reading the original, published, peer-reviewed research papers on algorithms for this problem.  I would expect them to generally be of relatively high quality, and to be more likely to explain the algorithm and methodology in full (or cite a source that does).